The document proposes a system to identify genetic diseases using multiple ontologies, regulatory modules between transcription factors, genes and microRNAs, and an integration technique. It uses cross ontology to classify genes by molecular function, cellular component and biological process. A multiplicative update algorithm then solves an optimization function to identify regulatory modules between miRNA, TF and genes. Finally, a Bayesian rose tree represents the identified diseases, symptoms and cures.
MicroRNA-Disease Predictions Based On Genomic Dataijtsrd
Gene Ontology is a structured library of concepts related with one or more gene products through a process called annotation. Association Rules that discovers biologically relevant and corresponding associations. In the existing system, they used Gene Ontology-based Weighted Association Rules for extracting annotated datasets. We here adapt the MOAL algorithm to mine cross-ontology association rules. Cross ontology rules to manipulate the Protein values from three sub ontologys for identifying the gene attacked disease. It focused on intrinsic and extrinsic values. The Co-Regulatory modules between microRNA, Transcription Factor and gene on function level with multiple genomic data. The regulations are compared with the help of integration technique. Iterative Multiplicative Updating Algorithm is used in our project to solve the optimization module function for the above interactions. Comparing the regulatory modules and protein value for gene and generating Bayesian rose tree for the efficiency of our result. Ajitha. C | DivyaLakshmi. K | Jothi Jayashree. M"MicroRNA-Disease Predictions Based On Genomic Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11386.pdf http://www.ijtsrd.com/computer-science/data-miining/11386/microrna-disease-predictions-based-on-genomic-data/ajitha-c
Analysis of gene expression microarray data of patients with Spinal Muscular ...Anton Yuryev
By examining experimental gene expression data researchers can identify potential upstream regulatory factors that may control key biological processes. In this paper we examine the effectiveness of two similar approaches to this type of identification using a publicly available data set from research done on Spinal Muscular Atrophy.
MicroRNA-Disease Predictions Based On Genomic Dataijtsrd
Gene Ontology is a structured library of concepts related with one or more gene products through a process called annotation. Association Rules that discovers biologically relevant and corresponding associations. In the existing system, they used Gene Ontology-based Weighted Association Rules for extracting annotated datasets. We here adapt the MOAL algorithm to mine cross-ontology association rules. Cross ontology rules to manipulate the Protein values from three sub ontologys for identifying the gene attacked disease. It focused on intrinsic and extrinsic values. The Co-Regulatory modules between microRNA, Transcription Factor and gene on function level with multiple genomic data. The regulations are compared with the help of integration technique. Iterative Multiplicative Updating Algorithm is used in our project to solve the optimization module function for the above interactions. Comparing the regulatory modules and protein value for gene and generating Bayesian rose tree for the efficiency of our result. Ajitha. C | DivyaLakshmi. K | Jothi Jayashree. M"MicroRNA-Disease Predictions Based On Genomic Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11386.pdf http://www.ijtsrd.com/computer-science/data-miining/11386/microrna-disease-predictions-based-on-genomic-data/ajitha-c
Analysis of gene expression microarray data of patients with Spinal Muscular ...Anton Yuryev
By examining experimental gene expression data researchers can identify potential upstream regulatory factors that may control key biological processes. In this paper we examine the effectiveness of two similar approaches to this type of identification using a publicly available data set from research done on Spinal Muscular Atrophy.
Particle Swarm Optimization for Gene cluster IdentificationEditor IJCATR
The understanding of gene regulation is the most basic need for the classification of genes within a DNA. These genes
within the DNA are grouped together into clusters also known as Transcription Units. The genes are grouped into transcription units
for the purpose of construction and regulation of gene expression and synthesis of proteins. This knowledge further contributes as
essential information for the process of drug design and to determine the protein functions of newly sequenced genomes. It is possible
to use the diverse biological information across multiple genomes as an input to the classification problem. The purpose of this work is
to show that Particle Swarm Optimization may provide for more efficient classification as compared to other algorithms. To validate
the approach E.Coli complete genome is taken as the benchmark genome.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
Proteins facilitates most biological processes in a cell, including gene expression, cell growth, proliferation, nutrient uptake, morphology, motility, intercellular communication and apoptosis.
Protein–protein interactions (PPIs) refer to physical contacts established between two or more proteins as a result of biochemical events.
These interactions are very important in our lives as any disorder in them can lead to fatal diseases such as Alzheimer’s and Creutzfeld- Jacob Disease.
The most well known example of Protein-Protein Interaction is between Actin and Myosin while regulating Muscular contraction in our body.
The protein –protein interaction have commonly been termed the ‘INTERACTOME’ by scientists.
Homo-Oligomers: Complexes having one type of protein subunits.
E.g. : PPIs in Muscle Contraction
Hetero-Oligomers: Complexes having multiple types protein subunits.
E.g. : PPI between Cytochrome Oxidase and TRPC3 (Transient receptor potential cation channels
Brief Introduction of Protein-Protein Interactions (PPIs)Creative Proteomics
For more information, please visit https://www.creative-proteomics.com/services/protein-protein-interaction-networks.htm. Protein-protein interactions play important roles in various biological processes. PPIs can be classified based on different factors, including composition, affinity, and lifetime.
Yeast two hybrid system for Protein Protein Interaction Studiesajithnandanam
Yeast Two Hybrid system uses a reporter gene to detect the interaction of pair of proteins inside the yeast cell nucleus. In the yeast Two Hybrid System, The interaction of target protein to the protein will bring together transcriptional activator, which then switches on the expression of reporter gene.
#INTRODUCTION OF PPIs
#EXAMPLE OF PPIs
#CLASSIFICATION OF PPIs
#IDENTIFICATION METHOD OF PPIs
#YEAST TWO HYBRID SYSTEM
#DATABASE OF PPIs
#APPLICATIONS OF PPIs
#FACTOR AFFECTING PPIs
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
A genome is an organism’s complete set of DNA or complete genetic makeup, The entire DNA complement. It describes the identity and the sequence of genes of an organism.
Genomics is the study of entire genomes(structure, function, evolution, mapping, and editing of genomes)
Executing the sequencing and analysis of entire human genome enables more rapid and effective identification of disease associated genes and provide drug companies with pre validated targets.
Proteomics is the systematic high-throughput separation and characterization of proteins within biological systems./ large scale study of protein and their functions.
Proteomics measures protein expression directly, not via gene expression, thus achieving better accuracy. Current work uses 2-dimensional polyacrylamide gel electrophoresis(2D- PAGE) and mass spectrometry.
New separation and characterization technologies, such as protein microarray and high throughput chromatography are being developed.
Particle Swarm Optimization for Gene cluster IdentificationEditor IJCATR
The understanding of gene regulation is the most basic need for the classification of genes within a DNA. These genes
within the DNA are grouped together into clusters also known as Transcription Units. The genes are grouped into transcription units
for the purpose of construction and regulation of gene expression and synthesis of proteins. This knowledge further contributes as
essential information for the process of drug design and to determine the protein functions of newly sequenced genomes. It is possible
to use the diverse biological information across multiple genomes as an input to the classification problem. The purpose of this work is
to show that Particle Swarm Optimization may provide for more efficient classification as compared to other algorithms. To validate
the approach E.Coli complete genome is taken as the benchmark genome.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
Proteins facilitates most biological processes in a cell, including gene expression, cell growth, proliferation, nutrient uptake, morphology, motility, intercellular communication and apoptosis.
Protein–protein interactions (PPIs) refer to physical contacts established between two or more proteins as a result of biochemical events.
These interactions are very important in our lives as any disorder in them can lead to fatal diseases such as Alzheimer’s and Creutzfeld- Jacob Disease.
The most well known example of Protein-Protein Interaction is between Actin and Myosin while regulating Muscular contraction in our body.
The protein –protein interaction have commonly been termed the ‘INTERACTOME’ by scientists.
Homo-Oligomers: Complexes having one type of protein subunits.
E.g. : PPIs in Muscle Contraction
Hetero-Oligomers: Complexes having multiple types protein subunits.
E.g. : PPI between Cytochrome Oxidase and TRPC3 (Transient receptor potential cation channels
Brief Introduction of Protein-Protein Interactions (PPIs)Creative Proteomics
For more information, please visit https://www.creative-proteomics.com/services/protein-protein-interaction-networks.htm. Protein-protein interactions play important roles in various biological processes. PPIs can be classified based on different factors, including composition, affinity, and lifetime.
Yeast two hybrid system for Protein Protein Interaction Studiesajithnandanam
Yeast Two Hybrid system uses a reporter gene to detect the interaction of pair of proteins inside the yeast cell nucleus. In the yeast Two Hybrid System, The interaction of target protein to the protein will bring together transcriptional activator, which then switches on the expression of reporter gene.
#INTRODUCTION OF PPIs
#EXAMPLE OF PPIs
#CLASSIFICATION OF PPIs
#IDENTIFICATION METHOD OF PPIs
#YEAST TWO HYBRID SYSTEM
#DATABASE OF PPIs
#APPLICATIONS OF PPIs
#FACTOR AFFECTING PPIs
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
A genome is an organism’s complete set of DNA or complete genetic makeup, The entire DNA complement. It describes the identity and the sequence of genes of an organism.
Genomics is the study of entire genomes(structure, function, evolution, mapping, and editing of genomes)
Executing the sequencing and analysis of entire human genome enables more rapid and effective identification of disease associated genes and provide drug companies with pre validated targets.
Proteomics is the systematic high-throughput separation and characterization of proteins within biological systems./ large scale study of protein and their functions.
Proteomics measures protein expression directly, not via gene expression, thus achieving better accuracy. Current work uses 2-dimensional polyacrylamide gel electrophoresis(2D- PAGE) and mass spectrometry.
New separation and characterization technologies, such as protein microarray and high throughput chromatography are being developed.
Bioinformatics Introduction and Use of BLAST ToolJesminBinti
Hi, I am Jesmin, studying MCSE. I think this file will help you if you want to know the basic information about Bioinformatics and the use of BLAST tool. The BLAST tool is the tool that matches the sequences of DNA,RNA and proteins.
A Systems Biology Approach to Natural Products ResearchHuda Nazeer
Explains the systems biology approach (holistic approach), its advantages and tools used compared to the reductionist approach in natural products research.
Introduction
Overview
Reductionist approach
Holistic approach
What is systems biology?
○ Advantages of Systems Biology
Tools of holistic approach
○ Proteomics, Transcriptomics and Metabolomics
Conclusion
References
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...Ellen Berg
Predictive Models for Mechanism of Action Classification from Phenotypic Assay Data – Application to Phenotypic Drug Discovery
Presentation at SLAS 2014 conference in San Diego, 21 January 2014
For more information, you can visit https://www.creative-proteomics.com/services/protein-post-translational-modification-analysis.htm. In this video, we introduce some commonly used methods to detect PPIs and techniques for proteome-scale interactome maps.
Bioinformatics, application by kk sahu sirKAUSHAL SAHU
INTRODUCTION
HISTORY
WHAT IS BIOINFORMATICS
APPLICATIONS
DNA AND RNA LEVELS
CONCLUSION
REFRENCES
"Bioinformatics" to refer to the study of information processes in biotic systems. This definition placed bioinformatics as a field parallel to biophysics or biochemistry (biochemistry is the study of chemical processes in biological systems).
the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Genetic disease identification and medical diagnosis using MF, CC, BF, MicroRNA and transcription factors
1. • PRESENTED BY GUIDED BY
• KARTHIGA R
Dr.ADLIN SHEEBA
• 312414104049
Associate Professor
Genetic disease identification and medical
diagnosis using MF, CC, BF, MicroRNA and
transcription factors
2. Abstract
• The Main aim of our project is to create and maintain a Genomic and Proteomic
Knowledge Base (GPKB), which integrates the most relevant sources of bio
information.
• Cross ontology is used to manipulate the Protein values from three sub ontologies
for identifying the gene attacked disease.
• Based on cellular component, molecular function and biological process values
intrinsic and extrinsic calculation would be manipulated.
• Comparing the regulations between miRNA-TF interaction, TF-gene interactions
and gene-miRNA interaction with the help of integration technique.
• Iterative Multiplicative Updating Algorithm is used to solve the optimization
module function for the above interactions.
• After that interactions compare the regulatory modules and protein value for gene
and generate Bayesian rose tree for efficiency of our result.
3. Existing system
• The eprocessxisting system used association rules mining algorithm to
support GO terms such as Molecular function, Cellular component,
Biological.
• It is used to find all relative genetic diseases without any accuracy.
• It is time consuming.
4. Proposed system
• Proposal of co-regulatory modules between Transcription Factor, gene and
MiRNA on functional level with genomic data.
• The integration technique is implemented between miRNA, Transcription
Factor (TF) and gene.
• After integration, Iterative Multiplicating update algorithm is used to check
the optimization function between the regulatory modules.
• Expression or some value from this algorithm is obtained and then
compared to protein values.
• The protein value is the one got from Biological Process (BP), Molecular
Function (MF) and Cellular Component (CC) with the help of cross
ontology technique.
• At last a bayesian rose tree structure is obtained that shows our disease
which was affected in our chromosome and also how to cure it. Also we
can identify the symptoms applicable for our gene by our proposed system.
5. Literature survey
SI.no Title Author Year Content
1 Ontology mining in
Molecular Biology
domain
Anuraag
Vikram Kate,
Harish
Balakrishnan
2016 Ontology mining is done, inorder to
improvise the prevailing ontology
functionally by the inclusion of
DNA and RNA components, and
also structurally by expanding the
ontology in a different perspective.
2 Using GO-WAR for
mining cross-
ontology weighted
association rules
Giuseppe
Agapito, Mario
Cannataro
2015 initially we calculate the
information content for each GO
term then, we extract weighted
association rules by using a
modified FP-Tree like algorithm
able to deal with the dimension of
classical biological datasets.
3 Improving
annotation quality
Cami de Vera. 2014 (GO-WAR), Mining Weighted
Association Rules from GO, that is
8. Module description
Cross Ontology:
• It classifies functions along three aspects: molecular function molecular
activities of gene products, cellular component where gene products are
active, biological process pathways and larger processes made up of the
activities of multiple gene products.
• Intrinsic and extrinsic diseases are identified.
• Based on cellular component, molecular function and biological process
values intrinsic and extrinsic calculation would be manipulated.
9. Collaborative Filtering:
• Calculates the protein value of human and normal value of
particular gene id.
• cross ontology process get the BP,CC&MF value for gene to identify
the gene that have Intrinsic or extrinsic diseases.
•Intrinsic:
If the normal protein value of human is compare to lower than that
of calculating cross ontology value (comparing BP&CC or MF&CC or
MF&BP) is said to be Intrinsic.
•Extrinsic:
If the normal protein value of human is compare to higher than
that of calculating cross ontology value (comparing BP&CC or MF&CC
or MF&BP) is said to be extrinsic.
10. Regulatory modules:
• Sets of genes are co-regulated to respond to different conditions.
• It identifies modules of co-regulated genes, their regulators and the
conditions under which regulation occurs, generating testable hypotheses in
the form 'regulator X regulates module Y under conditions W'.
11. Integration Technique:
•In this module, we use a fusion technique to integrate both gene ontology
and regulatory modules.
•This is the first time of proposing a fusion technique in gene analysis which
produces increased accuracy.
12. Multiplicative Update Algorithm:
• A novel approach to identify miRNAs and transcription factors co-
regulatory modules.
• An objective function is constructed by integrating the miRNA,TF,gene
expression profiles, target site information (miRNA-gene and TF-gene
regulations) as well as the protein-protein interactions.
• In order to obtain the optimal solution of the objective function, we solve
the optimization model function effectively by iterative multiplicative
updating algorithm.
13. • Tree Representation:
Bayesian Rose Tree gives us the structure of tree with genetic diseases, its
symptoms and how to cure the diseases.
