IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Multiscale integrative data analytics in pharmacogenomicsDr. Gerry Higgins
This strategy for mapping drug networks provides insight into the mechanistic on- and off-target effects, laying a foundation for subsequent preclinical studies.
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
Multiscale integrative data analytics in pharmacogenomicsDr. Gerry Higgins
This strategy for mapping drug networks provides insight into the mechanistic on- and off-target effects, laying a foundation for subsequent preclinical studies.
MINING OF IMPORTANT INFORMATIVE GENES AND CLASSIFIER CONSTRUCTION FOR CANCER ...ijsc
Microarray is a useful technique for measuring expression data of thousands or more of genes
simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data
is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the
distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and
robust gene identification methods is extremely fundamental. Many gene selection methods as well as their
corresponding classifiers have been proposed. In the proposed method, a single gene with high classdiscrimination
capability is selected and classification rules are generated for cancer based on gene
expression profiles. The method first computes importance factor of each gene of experimental cancer
dataset by counting number of linguistic terms (defined in terms of different discreet quantity) with high
class discrimination capability according to their depended degree of classes. Then initial important genes
are selected according to high importance factor of each gene and form initial reduct. Then traditional kmeans
clustering algorithm is applied on each selected gene of initial reduct and compute missclassification
errors of individual genes. The final reduct is formed by selecting most important genes with
respect to less miss-classification errors. Then a classifier is constructed based on decision rules induced
by selected important genes (single) from training dataset to classify cancerous and non-cancerous samples
of experimental test dataset. The proposed method test on four publicly available cancerous gene
expression test dataset. In most of cases, accurate classifications outcomes are obtained by just using
important (single) genes that are highly correlated with the pathogenesis cancer are identified. Also to
prove the robustness of proposed method compares the outcomes (correctly classified instances) with some
existing well known classifiers.
Integrative bioinformatics analysis of Parkinson's disease related omics dataEnrico Glaab
Presentation on statistical meta analysis of omics data from Parkinson's disease case-control studies. The results are used for a comparative analysis against aging-related omics alterations in the brain and a prioritization of new candidate disease genes using the phenologs approach.
Selection of genes to include in genomic studies of disease
remains a difficult task. Current methods rely on expert opinion
or manual search engine use. With these methods, the
process and result are neither repeatable nor scalable. To
remedy this situation, we created the Informative Genetic
Content (IGC) system, which enables the algorithmic selection
of genes for inclusion in such studies, given one or more
diseases to target.
The IGC system stands on three components: a database
associating diseases with genes and other diseases, an
algorithm to rank the genes under consideration for inclusion in
a panel, and a module that clusters genes by families of
diseases. The first component, the database, maps diseases
to associated genes and scores each of these mappings
according to the strength of the relationship. The database also
maps diseases to other diseases, such that groups of diseases
or hierarchical relationships between diseases can be
identified. The second component enables the ranking of
candidate genes when multiple diseases are of interest. The
algorithm accounts for the common situation where two or
more diseases are associated with the same gene with varying
strengths of association, weighting and combining the scores
across the diseases associated with each gene. The final
component, the gene clustering module, groups genes by
pathogenic pathways, should the user want to consider
targeting a broader family of diseases affected by a closely
related set of genes.
We validated the IGC system through comparisons of our
automated gene selections with expertly curated gene panel
designs. We found a high degree of overlap between the IGC’s
gene selection and the gene lists chosen by experts,
supporting the viability of our system.
Together with the scalability and repeatability enabled by its
automation, the IGC system greatly improves the gene panel
selection process and therefore advances targeted genomic
studies.
SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fu...IJECEIAES
In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
Clustering Approaches for Evaluation and Analysis on Formal Gene Expression C...rahulmonikasharma
Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.
Autologous Bone Marrow Cell Therapy for Autism: An Open Label Uncontrolled C...remedypublications2
The aim of this study is to assess the safety and effectiveness of autologous bone marrow
mononuclear stem cell (BMMNC) transplantation in patients with autism.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
A Rare International Dialogue (Sunday, May 12, 2019)
Theme One: Diagnosis and Beyond
WORKSHOP G: Cell and Gene Therapy from Laboratory to Market - Mark Lundie, Pfizer Canada
Stratification of TCGA melanoma patients according to Tumor Infiltrative CD8...Antonio Ahn
The tumour microenvironment, namely the interaction between immune cells and tumour cells plays a crucial role in the treatment outcome of immunotherapy.
In order to predict patient responses to immunotherapy a tumour stratification framework has been proposed based on PD-L1 expression and presence of CD8 Tumour Infiltrative Lymphocytes (TIL).
Advances in genomic technologies and computational tools now allow to determine compositions of different immune cell infiltrates in bulk tumors with increasing accuracy and resolution. Our aim here was to use RNA-seq and methylation 450k data to stratify 469 melanoma patients in TCGA dataset according to the presence of CD8 Tumour Infiltrative Lymphocytes (TIL) and PD-L1 mRNA expression.
Cell centered database for immunology and cancer research feb252016Ann-Marie Roche
Determining the cellular mechanisms of diseases is a crucial requirement for understanding the causes and progression of diseases, predicting outcomes, and developing new treatments. Often relevant information, e.g. what cells are involved in a disease or what effects does a drug have on cells, is scattered across many papers and journals, which makes it difficult for researchers to be sure they have a complete picture. Using Elsevier’s automated text mining technology, we have created a new cell-centered database consisting of 850 000 facts captured from more than 24 million PubMed abstracts and 3.5 million full text articles for use in Pathway Studio. This database focused primarily on cellular aspects of immunology and immuno-oncology can be used to summarize and visualize published research, and to analyze experimental data.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparative analysis of edge based and region based active contour using leve...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Integrative bioinformatics analysis of Parkinson's disease related omics dataEnrico Glaab
Presentation on statistical meta analysis of omics data from Parkinson's disease case-control studies. The results are used for a comparative analysis against aging-related omics alterations in the brain and a prioritization of new candidate disease genes using the phenologs approach.
Selection of genes to include in genomic studies of disease
remains a difficult task. Current methods rely on expert opinion
or manual search engine use. With these methods, the
process and result are neither repeatable nor scalable. To
remedy this situation, we created the Informative Genetic
Content (IGC) system, which enables the algorithmic selection
of genes for inclusion in such studies, given one or more
diseases to target.
The IGC system stands on three components: a database
associating diseases with genes and other diseases, an
algorithm to rank the genes under consideration for inclusion in
a panel, and a module that clusters genes by families of
diseases. The first component, the database, maps diseases
to associated genes and scores each of these mappings
according to the strength of the relationship. The database also
maps diseases to other diseases, such that groups of diseases
or hierarchical relationships between diseases can be
identified. The second component enables the ranking of
candidate genes when multiple diseases are of interest. The
algorithm accounts for the common situation where two or
more diseases are associated with the same gene with varying
strengths of association, weighting and combining the scores
across the diseases associated with each gene. The final
component, the gene clustering module, groups genes by
pathogenic pathways, should the user want to consider
targeting a broader family of diseases affected by a closely
related set of genes.
We validated the IGC system through comparisons of our
automated gene selections with expertly curated gene panel
designs. We found a high degree of overlap between the IGC’s
gene selection and the gene lists chosen by experts,
supporting the viability of our system.
Together with the scalability and repeatability enabled by its
automation, the IGC system greatly improves the gene panel
selection process and therefore advances targeted genomic
studies.
SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fu...IJECEIAES
In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
Clustering Approaches for Evaluation and Analysis on Formal Gene Expression C...rahulmonikasharma
Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.
Autologous Bone Marrow Cell Therapy for Autism: An Open Label Uncontrolled C...remedypublications2
The aim of this study is to assess the safety and effectiveness of autologous bone marrow
mononuclear stem cell (BMMNC) transplantation in patients with autism.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
A Rare International Dialogue (Sunday, May 12, 2019)
Theme One: Diagnosis and Beyond
WORKSHOP G: Cell and Gene Therapy from Laboratory to Market - Mark Lundie, Pfizer Canada
Stratification of TCGA melanoma patients according to Tumor Infiltrative CD8...Antonio Ahn
The tumour microenvironment, namely the interaction between immune cells and tumour cells plays a crucial role in the treatment outcome of immunotherapy.
In order to predict patient responses to immunotherapy a tumour stratification framework has been proposed based on PD-L1 expression and presence of CD8 Tumour Infiltrative Lymphocytes (TIL).
Advances in genomic technologies and computational tools now allow to determine compositions of different immune cell infiltrates in bulk tumors with increasing accuracy and resolution. Our aim here was to use RNA-seq and methylation 450k data to stratify 469 melanoma patients in TCGA dataset according to the presence of CD8 Tumour Infiltrative Lymphocytes (TIL) and PD-L1 mRNA expression.
Cell centered database for immunology and cancer research feb252016Ann-Marie Roche
Determining the cellular mechanisms of diseases is a crucial requirement for understanding the causes and progression of diseases, predicting outcomes, and developing new treatments. Often relevant information, e.g. what cells are involved in a disease or what effects does a drug have on cells, is scattered across many papers and journals, which makes it difficult for researchers to be sure they have a complete picture. Using Elsevier’s automated text mining technology, we have created a new cell-centered database consisting of 850 000 facts captured from more than 24 million PubMed abstracts and 3.5 million full text articles for use in Pathway Studio. This database focused primarily on cellular aspects of immunology and immuno-oncology can be used to summarize and visualize published research, and to analyze experimental data.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparative analysis of edge based and region based active contour using leve...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Design and development of non server peer 2 peer secure communication using j...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Pi controller based of multi level upqc using dq0 transformation to improve p...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The heating pattern of the microwave dehydrator for treating petroleum crude ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Disease Network is the science that has emerged to diagnose a disease from a network aspect
specifically. Networks are the group that interconnect to each others similarly disease networks are
the one that reveal concelled connection among apparently independent biomedical entities like
physiologic process, signaling receptors, in addition to genetic code, also they prove to exists
intitutive in addition to powerful way to learn/discover or diagnose a disease.Due to these networks,
we can now consume the elderly drugs and its method to learn/discover the new drug
accordingly.Example- Colchicine is used in gout but after repurposing it is also used in mediterranean
fever. This is because there are many factors that affect the body during mediterranean fever and
gout, we know that gout is a form of arthritis that causes pain in joints also mediterranean fever is the
one which is accompanied by pain in joints, therefore colchicine is used as a repurposed drug again.In
repurposing of medicines or drugs we first analyse the change in symptoms and identify the target
organ and accorgingly we produce a drug that is compatible with pharmacokinetics of the body. As
the availablity of transcriptomic,proteomic and metabolomic data sources are increasing day by day it helps in classification of disease .Also there are some networks reffered to as complex networks which can be called as collection of linked junctions/ nodes
A radiology report serves as an intermediary between a radiologist and referring clinician for suggesting
appropriate treatment to the patients, aimed at better healthcare management. It is essentially a tool
that assists radiologists in conveying their input to the patients and clinicians regarding positive or negative findings on a case. The objective of this paper is to discuss and propose Radiology Information & Reporting System (RIRS), highlight challenges governing its implementation and suggest way forwards
towards its effective implementation across the public sector tertiary care institutions of Pakistan. In the end, it is concluded that the proposed RIRS would potentially offer enormous benefits in terms of cost
savings, reporting accuracy, faster processing and operational efficiency as opposed to the conventionally available manual radiology reporting procedures and systems.
CXCL1, CCL20, STAT1 was Identified and Validated as a Key Biomarker Related t...semualkaira
Growing evidence suggests a correlation between ulcerative colitis (UC) and immune markers. Pathogenesis of UC was not yet been clearly elucidated, and few researches on immune-related biomarkers published.
CXCL1, CCL20, STAT1 was Identified and Validated as a Key Biomarker Related t...semualkaira
Growing evidence suggests a correlation between ulcerative colitis (UC) and immune markers. Pathogenesis
of UC was not yet been clearly elucidated, and few researches on
immune-related biomarkers published.
Review on Computational Bioinformatics and Molecular Modelling Novel Tool for...ijtsrd
Advancement in science and technology has brought a remarkable change in the field of drug discovery. Earlier it was very difficult to predict the target for receptor but nowadays, it is easy and robust task to dock the target protein with ligand and binding affinity is calculated. Docking helps in the virtual screening of drug along with its hit identification. There are two approaches through which docking can be carried out, shape complementary and stimulation approach. There are many procedures involved in carrying out docking and all require different software's and algorithms. Molecular docking serves as a good platform to screen a large number of ligands and is useful in Drug-DNA studies. This review mainly focuses on the general idea of molecular docking and discusses its major applications, different types of interaction involved and types of docking. Rishabh Jain "Review on Computational Bioinformatics and Molecular Modelling: Novel Tool for Drug Discovery" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18914.pdf
http://www.ijtsrd.com/pharmacy/pharmacoinformatics/18914/review-on-computational-bioinformatics-and-molecular-modelling-novel-tool-for-drug-discovery/rishabh-jain
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Bioinformatics in the Clinical Pipeline: Contribution in Genomic Medicineiosrjce
In this review report we like to focus on the new challenges in methodology of modern biology be
used in medical science. Today human health is a primary issue to cure disease, undoubtedly the answer to this
is bioinformatics or (In-silco) tools has change the concept of treating patients to understand the need of
genomic medicine in use. Those with new modes of action in clinical treatment, is a major health concern in
medical science. On global prospective scientific role in constructing new ideas to remediate health care to
treat disease exciting in nature is challenging task. So awareness needs to accelerate store clinical datasets for
scientific represents to design genomic drugs. This new outline will drive the medical to discover public data
and create a cognitive approach to use technology cheaper at cost effective mode.
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
ABSTRACT
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
INNOVATIVE MEDICINES, TECHNOLOGIES AND APPROACHES FOR IMPROVING PATIENTS' HE...Jing Zang
Despite remarkable scientific and technological achievements during the 20th century, the 21st century has already witnessed additional new and profound changes in all areas of medical science and research, including innovations and discoveries in biology, cellular biology, genomics and proteomics, pharmaceuticals, medical devices, and information technology. This review is an up-date on some of the existing therapies, drug delivery technologies, and approaches that aimed to improve patients’ health care and quality of their life.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 105
UNRAVELLING THE MOLECULAR LINKAGE OF CO-MORBID
DISEASES: DIABETES MELLITUS, HYPERTENSION AND CORONARY
ARTERY DISEASE
Varun Moganti1
, Abhinandita Dash2
1
Department of Biotechnology, University College of Engineering,JNTU-Kakinada,India
2
Department of Biology, Prof CR.Rao AIMSCS, University of Hyderabad Campus, India
Abstract
ABSTRACT : The incidence of Diabetes Mellitus (DM), Hypertension (HTN) and Coronary artery disease (CAD) in the country has
increased alarmingly. Since decades DM and HTN have been proved to be independent risk factors for CAD. Gene and its regulatory
action through a protein are vital for the normal metabolism. Any abnormality in regulation would lead to a disease. Our study used
the principles of network biology to understand the comorbidity of diseases at the molecular level. We have collected disease genes of
DM, HTN and CAD from various public databases and extracted genes common to all the three diseases. We constructed a biological
network by considering the protein interaction data obtained from Human Protein Reference Database (HPRD).The network was
validated using power law distribution and the genes were ranked using Centiscape. Finally we identified the crucial genes with
literature validation which could play a major role in causing disease co-morbidity.
Keywords –Biological Network, Coronary Artery Disease, Diabetes Mellitus, Hypertension and Systems Biology
----------------------------------------------------------------------***------------------------------------------------------------------------
1. INTRODUCTION
Diabetes Mellitus and Hypertension are well established
independent risk factors for Coronary artery Disease. The co-
existence of diabetes and hypertension would have negative
impact on the prognosis and outcome of a patient. The
presence of hypertension would accelerate the risk of coronary
artery diseases. [Ref1] The underlying key pathophysiological
mechanism would be vascular endothelial dysfunction, platelet
aggregation and platelet dysfunction. [Ref2] The main goal of
a physician is to maintain the blood glucose level and
normotensive state in order to reduce the risk of
cardiovascular diseases. There is always a chance of an
economic burden for the treatment of diabetes mellitus and
hypertension considering the existing scenario of our country.
So the major focus on treatment modality would be
prophylactic to prevent the risk of other disease prevalence.
The central dogma of molecular biology is Deoxyribonucleic
Acid (DNA) which upon transcription produce Ribonucleic
Acid (RNA) and RNA expresses or regulates a particular
function through a protein. Proteins are major molecules that
co-ordinate, regulate many functions in our body through
various mechanisms. Protein interacts with other protein to
perform many metabolic activities. A lot of credit goes in
favour with the advent of high throughput experiments leading
to lot of protein-protein interaction data.[Ref3]The well
established high throughput experiments are the pull down
assays, tandem affinity purification(TAP), yeast two
hybrid(Y2H), mass spectrophotometry, microarrays, phage
displays.[Ref4,12] There are many public repositories
available which gives the interacting partners of the
genes/protein. Protein-Protein Interaction (PPI) data sets could
be retrieved from BIND [Ref5], STRING [Ref6], MINT
[Ref7], DIP [Ref8], HPRD [Ref9] and many others.
Graph theory has wide range of applications in fundamental
areas of mathematics, statistics, physics, chemistry and
biology. As we all understand that human biological
phenomenon is highly complex and it could be very well
studied in elementary constituents, these elementary
constituents interact in their own manner to bring about a
normal regulation. For example the energy regulation through
generation of ATP molecules are regulated in a chain like
phenomenon such as Kreb’s cycle which is in fact is a
molecular and a metabolic phenomenon. Any disruption to the
normal physiology leads to an abnormality which could be a
pathophysiologic condition. All these reactions could be
represented as a network. This approach of study is called as
Systems Biology or Network Biology. This is a rapidly
growing domain in bioinformatics which deals with Biological
Networks. [Ref10]
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 106
There are various types of biological networks such as Gene
Regulatory Networks, Cell Signaling Networks, PPI networks,
Metabolic Networks and so on. [Ref11]. In the current
scenario, PPI networks play a major role in Human Medicine
and Molecular Biology. The application of protein interaction
networks are: prediction of novel disease genes, prediction of
genotype-phenotype associations, studying the genetic and
molecular basis of diseases. [Ref12, Ref13]
A task of linking disease genes and associated human
disorders is done to develop a human diseasome. This was
constructed by using a bipartite graph between disease
phenome and disease genome. Few key observations
summarized in the study were genes contributing to a common
disease have increased tendency to participate in PPI,
tendency to express in a specific tissue, tend to display co-
expression levels. [Ref14]
Human diseasome exploration would connect the small world,
i.e. genes, proteins with global networks i.e. the diseases,
mortalityand patient care. The overall benefit of studying the
network would give rise to another domain called the
“Network Medicine”. This would help the physicians and the
biologists in understanding and reframe the existing treatment
methods of a patient. This would benefit the patient care and
would give birth to a highly specialized domain called the
genomic medicine or personalized medicine. [Ref15]
Centrality measures are mostly computed in network biology
depending upon the type of networks. The widely used
network centralities are Degree, Eccentricity, Betweenness,
Closeness, Eigen Vector, Radiality, Centroid value, Stress and
so on. The biological significance of the centrality measures is
well characterised and analysed as studied by Giovanni.
[Ref16]
2. RELATED WORK:
The major challenge faced by biomedical researcher is
prediction, identification of novel genes and protein function
prediction. Role of biological networks particularly PPI and
gene regulatory networks with the application of centrality
measures have proved significant in various studies performed
earlier. Potential of disease gene prediction and novel
candidate gene identification could be possible by exploiting
PPI networks. [Ref17]
Degree, Page Rank, Shortest path Betweenness, Closeness,
Radiality, Integration, Katz Status Index and Motif based
centralities were considered to be best centrality measures for
gene regulatory networks. Top two per cent of the genes were
considered as key regulatory players in gene regulatory
network. [Ref18]For disease gene prioritization considering
degree centrality would not favour the loosely connected or
disconnected nodes within the network. Several statistical
adjustment schemes were conducted to improve the
performance of the Degree centrality. [Ref19]
Two hundred and seventy six novel cardiovascular candidate
genes were identified by using a combined centrality measures
such as Degree, Betweenness, Neighbour count of Disease
gene, ratio of disease gene in neighbour, clustering co-efficient
and Mean shortest path length of disease. [Ref20]
An attempt was made in ranking candidate genes and
prioritizing them based on the microarray datasets and protein
interaction network. Katz centrality index was proposed to this
study and is applied for 40 diseases in 58 gene expression
datasets. [Ref21] A database is developed on Autism and
related neurological disease called Autworks. This is web
application developed to view autism gene network structure
within and related disorders associated with autism. [Ref22]
New novel genes were identified in prostate cancer, by using
centrality measures such as Degree, Eigen vector,
Betweenness and Closeness. The data was extracted by using
literature mining by using support vector machines. [Ref23]
A recent advance in applying Network medicine was drug
repurposing or drug repositioning. It was conducted in two
steps by constructing the protein interaction network with the
common genes shared by two diseases. The second step would
be identifying the drug target’s (gene/protein) presence within
the protein interaction network. The presence of the drug
target would make it a potential drug repositioning candidate.
This would pave way to a rational approach in treating a
multiple diseases with a single. [Ref24]
This paper aims to select three diseases which have a high rate
of co-morbidity, identify the common genes, extract their
protein interacting partners, constructing a protein interaction
network and apply centrality measures to rank the crucial role
players of the genes.
3. MATERIALS & METHODS:
3.1 Disease Gene Collection:
Genes known to be associated with the three different diseases
(DM, CAD, HTN) were collected from various databases. The
databases used are in Table1.
Table1: List of Databases used for Collection of Disease
Genes
DATABASE DM HTN CAD
GeneCards
[Ref25]
Y Y Y
Eugenes
[Ref26]
Y Y Y
OMIM [Ref27] Y Y Y
Entrez [Ref28] Y Y Y
Ensebml Y Y Y
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 107
[Ref29]
T-HOD [Ref30] Y Y N
Uniprot [Ref31] Y Y Y
KEGG [Ref32] Y Y Y
HuGe
Navigator
[Ref33]
Y Y Y
GeneAtlas
[Ref34]
Y Y Y
BioGUO
[Ref35]
N N Y
DDBJ [Ref36] N Y Y
GAD [Ref 37] Y Y Y
3.2 Extraction of common genes:
Common genes for these three diseases were extracted using
SQL query.
3.3 Interacting partners:
The interacting partners of the genes/proteins were collected
using HPRD [Ref9].
3.4 Network Construction & Visualization:
Cytoscape, [Ref38] is an online visualization tool, used to
construct a network. The network is constructed by removing
the duplicate edges and self-loops.
3.5 Computation of network parameters:
Network parameters are computed using NetworkAnalyzer
[Ref39]. It is a Cytoscape plugin that computes and displays a
comprehensive set of topological parameters and centrality
measures for undirected and directed networks, which includes
the number of nodes, edges, and connected components, the
network diameter, radius, density, centralization,
heterogeneity, clustering coefficient, and the characteristic
path length.
3.6 Computation of the centrality measures:
The centrality measure gives the significance of a particular
node in the network. CentiScaPe, [Ref 40] a Cyoscape plugin,
allows the user to compute the parameters in the network. In
our analysis, we computed Degree, Closeness, Betweenness
and Centroid value centralities after understanding the
biological significance of the genes with respect to the
diseases.
3.7 Ranking the genes:
The top hundred genes of each computed centrality measure
were considered and a cumulative score was obtained by
summing the independent scores of each centrality measures
as considered in our study. The genes with maximum score
were ranked higher.
3.8 Validation of the ranked genes:
The genes obtained were validated for literature based
evidence through PubMed citation index number (PMID No.).
The genes having evidence in all the three diseases were
identified as comorbid genes.
RESULTS & DISCUSSION:
The number of genes for Diabetes Mellitus was four thousand
three hundred and fourteen genes (4314). Hypertension was
two thousand six hundred and seventy five(2675) and for
Coronary Artery Disease was one thousand six hundred and
ninety two (1692). A total of seven hundred and five four
genes (754) were identified to be common among all the three
diseases. These genes were extracted using SQL query. The
nomenclature and the chromosomal position of these genes
were listed using HGNC.[Ref41] The interacting proteins were
collected from HPRD.
FIGURE1: Biological Network constructed using Cytoscape
The network was constructed and visualized using Cytoscape.
The number nodes and edges were four thousand two hundred
and eighty seven (4285) and ten thousand four hundred and
sixty nine (10169) respectively. The network generated was
complex. The network was further analysed using Network
Analyzer, a Cytoscape plugin. The simple parameters
generated are mentioned in the Table 2. The network was
validated using power law distribution. Network Analyzer fits
power-law by using least squares method. The parameters of
power-law fit are:
y=axb
a=1234.2 b=-1.536
Correlation=0.998 R-squared=0.882
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 108
TABLE 2: List of Simple Parameters
S.No. Parameter Value
1 No. of connected
components
49
2 Clustering co-efficient 0.093
3 Network Diameter 11
4 Network Radius 1
5 Network Density 0.001
6 Characteristic Path
Length
4.190
7 Neighbourhood
connectivity
4.883
The centrality measures were computed using CentiScape
plugin of Cytoscape. Degree, Closeness, Betweenness and
Centroid value were selected based upon the understanding of
their biological significance particularly in a disease state. We
selected only hundred genes falling in each centrality measure.
Each gene was assigned a score and a rank was obtained based
on the cumulative score of those centrality measures. Initially,
we obtained a set of fifty five (55) genes that were ranked by
each centrality measure. We validated those genes obtained,
with literature evidence by using PubMed citation index.
Finally we could identify a total of ten (10) genes showing
evidence in all the three diseases.
In this study, we could identify genes which could lead to the
co-morbidity among three diseases. These genes are to be
studied in detail to understand their mechanisms and pathways
associated in each disease. Their physiological role is also to
be understood in order to have a clear picture on co-morbidity
mechanisms. These genes could be analysed for expression
analysis in every disease state to determine the
pathophysiologic role of the gene. After expression analysis
studies and their results, the following work could be taken
one step ahead for therapeutic studies. Drug targets can be
identified, prophylactic drugs can be developed, most
significantly developing stage of other disease could be
predicted and prevention of this disease could be possible.
CONCLUSION:
The dynamics of network biology has lead to many
applications in the field of biology and medicine. Drug target
repositioning, is now a much explored field as the concept of
single drug-multiple diseases gained in lot of importance. This
paper is a small attempt to understand the comorbid genes
associated with three diseases and trying to identify the top
genes with the application of graph theory principles and
protein interaction networks. The novelty in our approach is to
reduce the impact of complex network and extrapolating the
comorbid genes and their role within the protein interaction
network. We used the well studied centrality measures such as
Degree, Betweenness, Closeness and Centroid value for
analysing the networks based on their biological significance
we adopted a ranking system by considering the cumulative
scores of each centrality measure and assigned rank
accordingly.
The efficacy of this work would be determined if people
understand the significance of genes that play a role in
comorbidity, analyse the expression data and evaluate for
potential drug targets.
ACKNOWLEDGEMENTS:
Facilities provided by C.R.Rao Advanced Institute of
Mathematics, Statistics and Computer Sciences, Idea and
Encouragement by Dr.Ramesh Naidu ,Associate Professor of
Medicine, King George Hospital, Andhra Medical College,
Prof. Col.Allam Appa Rao Director -AIMSCS and Mr.Ramesh
Malothu, Head Of Department , Department of Biotechnology,
JawaharLal Nehru Technological University-Kakinada
SUPPLEMENTARY INFORMATION:
Supplementary Information 1:The common genes of all the
three diseases, nomenclature & chromosomal location
Supplemenatry Information 2:Top 100 genes ranked by
each centrality measure
Supplementary Information 3:Comorbid genes validated
through literature evidence (PMID citation number)
REFERENCES
[1] M..Epsteinand J.R Sowers, Diabetes mellitus and
hypertension, Hypertension, 19(50),1992, 403-418
[2] J.R.Sowers and M.Epstein, Diabetes Mellitus and
Associated Hypertension, Vascular Disease and Nephropathy-
An Update, Hypertension, 37, 1991,1053-59
[3] M G. Kann, Protein interactions and disease:
computational approaches to uncover the etiology of diseases,
Briefings in Bioinformatics,8(5), 2007, 333-346
[4] Trey Ideker andRodedSharan, Protein networks in
disease, Genome Research, 18,2008, 644-652
[5] Bader GD, Betel D & Hogue CW, BIND: the
Biomolecular Interaction Network, Nucleic Acids Research,
Database,31,2003,248-250
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 109
[6] Szklarczyk D, Franceschini A, Kuhn M, Simonovic
M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P,
Jensen LJ, von Mering C,The STRING database in:functional
interaction networks of proteins globally integrated and
scored, Nucleic Acids Research,(Database issue), 39,
2011,D561-568
[7] Ceol A, ChatrAryamontri A, Licata L, Peluso D,
Briganti L, Perfetto L, Castagnoli L &Cesareni G, MINT, the
molecular interaction database: update, Nucleic Acids
Research (Database issue), 2009, D532-D539.
[8] Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie
JU & Eisenberg D, The Database ofInteracting Proteins:
update, Nucleic Acids Research, 32,2004 D449-451
[9] Prasad TSK, Goel R, Kandasamy K, Keerthikumar
S, Kumar S, Mathivanan S, Telikicherla D,Raju R, Shafreen
B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee
S,Somanathan DS, Sebastian A, Rani S, Ray S, Harrys
Kishore CJ, Kanth S, Ahmed M,Kashyap MK, Mohmood R,
Ramachandra YL, Krishna V, Rahiman BA, Mohan
S,Ranganathan P, Ramabadran S, Chaerkady R &Pandey A,
Human ProteinReference Database – Update, Nucleic Acids
Research,37, 2009, D767-72.
[10] Georgios A Pavlopoulos, Maria Secrier, Charalampos
N Moschopoulos, Theodoros G Soldatos, Sophia Kossida, Jan
Aerts, Reinhard Schneider and Pantelis G Bagos, Using graph
theory to analyze biological networks, BioDataMining,4(10).
2011, 1-27
[11] Xiaowei Zhu, Mark Gerstein and Michael Snyder,
Getting connected: analysis and principles of biological
networks, Genes Development, 21,2007, 1010-1024.
[12] M.W. Gonzalez, M. G. Kann, Protein interactions and
Disease, PLOS Computational Biology, 8(12), 2012, 1-12
[13] Marc Vidal, Michael E. Cusick, and Albert-La´ szlo´
Baraba´ si, interactome Networks and Human Disease,
Cell,144,2011,986-998
[14] Kwang-Il Goh, Michael E. Cusick, David Valle,
Barton Childs, Marc Vidal and Albert-La´ szlo´ Baraba´ si
,Human Disease Network, PNAS,104(21).2007 8685-8690.
[15] Frank Emmert-Streib, ShaileshTripathi,Ricardo de
Matos Simoes, Ahmed F. Hawwa and Matthias Dehmer, The
human disease network, Systems Biomedicine, 1(1),2013,1-8
[16] Giovanni Scardoni and Carlo Laudanna ,Centralities
Based Analysis of Complex Networks, Dr.Yagang Zhang
(Ed.), New Frontiers in Graph Theory,2012, ISBN: 978-953-
51-0115-4, InTech, DOI: 10.5772/35846.
[17] M Oti, B Snel, M A Huynen, H G Brunner,
Predicting disease genes using protein-protein interactions,
Journal of Medical Genetics, 43,2006,691-698
[18] SinanErten and Mehmet Koyuturk, Role of Centrality
in Network-Based Prioritization of Disease Genes,
Evolutionary Computation, Machine Learning and Dats
Mining I Bioinformatics, Lecture Notes in Computer Science,
6023,2010,13-25
[19] Dirk Koschützki and Falk Schreiber, Centrality
Analysis Methods for Biological Networks and Their
Application to Gene Regulatory Networks, Gene Regulation
and Systems Biology, 2,2008, 193-201
[20] Liangcai Zhang, Xu Li, Jingxie Tai, Wan Li and Lina
Chen, Predicting Candidate Genes Based on
CombinedNetwork Topological Features: A Case Study in
Coronary Artery Disease, PLoS ONE, 7(6),2012,1-12
[21] Jing Zhao, Ting-Hong Yang, Yongxu Huang and
PetterHolme, Ranking Candidate Disease Genes from Gene
Expression and Protein Interaction: A Katz-Centrality Based
Approach, PLoS ONE, 6(9). 2011, 1-9
[22] Tristan H Nelson, Jae-Yoon Jung, Todd F DeLuca1,
Byron K Hinebaugh, KristianChe St. Gabriel and Dennis P
Wall,Autworks: a cross-disease network biology application
for Autism and related disorders, BMC Medical Genomics,
5(56). 2012, 1-4
[23] Arzucan Özgür1, Thuy Vu1, Günes¸ Erkan1 and
Dragomir R. Radev, Identifying gene-disease associations
using centrality on aliterature mined gene-interaction network,
Bioinformatics,24.2008, i277-285
[24] Yutaka Fukuoka, Daiki Takei and Hisamichi Ogawa,
A two-step drug repositioning method based on a protein-
protein interaction network of genes shared by two diseases
and the similarity of drugs, Bioinformation,9(2).2013,89-93
[25] Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet
D,GeneCards: integrating information about genes, proteins
and diseases, Trends Genetics,13 (4), 1997 163-167
[26] D.G Gilbert, euGenes: A eukarytotic genome
information system, Nucleic Acids Research, 30. 2002, 145-
148
[27] Hamosh, A.; Scott, A. F.; Amberger, J. S.; Bocchini,
C. A.; McKusick, V. A, Online Mendelian Inheritance in Man
(OMIM), a knowledgebase of human genes and genetic
disorders, Nucleic Acids Research (Database issue)33, 2004,
D514–D517.
[28] Donna Maglott, Jim Ostell, Kim.D.Pyuitt and
TatiuvaTatusova “EntrezGene: gene centred information,
Nucleic Acid Research (Database issue), 33, 2005, D54-58
[29] Flicek P, Amode MR, BarrellD,Ensembl 2011,
Nucleic Acids Research (Database issue)39,2010, D800–D806
[30] Hong-Jie Dai, Johnny Chi-Yang Wu, Richard Tzong-
Han Tsai, Wen-Harn Pan, and Wen-Lian Hsu, T-HOD: A
literature-based candidate gene database for hypertension,
obesity and diabetes, Database(Oxford), 2013
[31] Universal Consortium: Ongoing and future
developments at the Universal Protein Resource, Nucleic
Acids Research (Database issue),39, D214-D219
[32] Kanehisa M, Goto S, Kawashima S, Okuno Y,
Hattori M,The KEGG resource for deciphering the genome,
Nucleic Acids Research (Database issue),32, D277–80.
[33] W Yu, M Gwinn, M Clyne, A Yesupriya& M J
Khoury, A Navigator for Human Genome Epidemiology,
Nature Genetics, 40(2), 2008,124-5.
[34] Andrew I.Se, Tim Wiltshire, Serge Batalov, Hilmar
Lapp, Keith A.Ching, David Block, Jia Zhang, Richard Soden,
Mimi Hayakawa, Gabriel Krieman, Michael.P.Cooke,
John.R.Walker and John B. Hogenesch, A GeneAtlasof mouse
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 110
and human protein encoding transcriptiomes, PNAS,101(16),
6062-67
[35] Hui Liu, Wei Liu, Yitug Liao, langCheng,Qian Liu,
Xiang Ren, Lisag Shi, XinTu,Qiy Kenneth Wang, An-Yuan
Guo,CADgene: a comprehensive database for coronary artery
disease genes, Nucleic Acids Research (Database issue),39,
2011,991-6
[36] Tateno Y, Imanishi T, Miyazaki S, Fukami-
Kobayashi K, Saitou N, Sugawara H,DNA Data Bank of Japan
(DDBJ) for genome scale research in life science, Nucleic
Acids Research,30 (1),2002, 27–30
[37] KG Becker, K.C Barnes, T.J Bright, S.A Wang, The
Genetic Association Database, Nature Genetics, 36,2004, 431
- 432
[38] Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A.,
Landys, N., Workman, C., Christmas, R., Avila-Campilo, I.,
Creech, M., Gross, B., Hanspers, K., Isserlin, R., Kelley,
R.,Killcoyne, S., Lotia, S., Maere, S., Morris, J., Ono, K.,
Pavlovic, V., Pico, A. R., Vailaya, A., Wang, P.-L. L., Adler,
A., Conklin, B. R., Hood, L., Kuiper, M., Sander, C.,
Schmulevich, I., Schwikowski, B., Warner, G. J., Ideker, T. &
Bader, G. D, Integration of biological networks and gene
expression data using cytoscape, Nature protocols, 2(10),2007
2366–2382.
[39] Assenov, Y., Ramirez, F., Schelhorn, S.-E.,
Lengauer, T. & Albrecht, M, Computing topological
parameters of biological networks, Bioinformatics 24(2),
2008, 282–284.
[40] Scardoni, G., Petterlini, M. &Laudanna, C,Analyzing
biological network parameters with CentiScaPe,
Bioinformatics, 25(21), 2009, 2857–2859.
[41] Gray KA, Daughterty LC, Gordon SM, Real RC,
Wright MW, Bruford EA, Genenames.org: the HGNC
resources, Nucleic Acid Research(Database) 41, 2013,
545:552