An integrated approach to analyzing breast cancer at the proteomic and genomic level is presented using a cytometric readout. The approach analyzes fine needle aspirates to assess proteins, mRNA expression of HER2, and genomic integrity. Feasibility testing used a model system of mixed cell lines and analyzed 40 breast tumors and 10 normal tissues fixed in two solutions. The clinical performance relates to the model system and the cell-based assay could apply to xenograft models and circulating tumor cells.
This presentation contain information about molecular biology and laboratory technics, specially alternative splicing.
all of them to try to explain cancer etiology, give on the molecular bases.
This presentation contain information about molecular biology and laboratory technics, specially alternative splicing.
all of them to try to explain cancer etiology, give on the molecular bases.
An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come.
This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective.
Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology.
Analysis of such data is extremely important as it leads to knowledge discovery that can be validated by experiments. Previously, the diagnosis of complex genetic diseases has conventionally been done based on the non-molecular characteristics like kind of tumor tissue, pathological characteristics, and clinical phase.
The microarray data can be well accounted for high dimensional space and noise. Same were the reasons for ineffective and imprecise results. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data.
While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis.
http://kaashivinfotech.com/
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http://kernelmind.com/
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http://inplanttrainingchennai.com/
OncoPrime - Patient derived platform for Biologics research Rachna Goyal
“OncoPrime™ platform uses patient-derived primary samples that offer clinically-relevant genotypic and
phenotypic diversity that translates better to the clinic vis-à-vis cell lines.
Functional assays, when adapted to OncoPrime™ can not only help in claiming physiological relevance
but also add additional layers of information for more informed clinical trials.
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An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come.
This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective.
Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology.
Analysis of such data is extremely important as it leads to knowledge discovery that can be validated by experiments. Previously, the diagnosis of complex genetic diseases has conventionally been done based on the non-molecular characteristics like kind of tumor tissue, pathological characteristics, and clinical phase.
The microarray data can be well accounted for high dimensional space and noise. Same were the reasons for ineffective and imprecise results. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data.
While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis.
http://kaashivinfotech.com/
http://inplanttrainingchennai.com/
http://inplanttraining-in-chennai.com/
http://internshipinchennai.in/
http://inplant-training.org/
http://kernelmind.com/
http://inplanttraining-in-chennai.com/
http://inplanttrainingchennai.com/
OncoPrime - Patient derived platform for Biologics research Rachna Goyal
“OncoPrime™ platform uses patient-derived primary samples that offer clinically-relevant genotypic and
phenotypic diversity that translates better to the clinic vis-à-vis cell lines.
Functional assays, when adapted to OncoPrime™ can not only help in claiming physiological relevance
but also add additional layers of information for more informed clinical trials.
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Building Enterprise Search Engines using Open Source TechnologiesAnant Corporation
Enterprise Search is a challenging problem for most organizations. Public search technologies such as Google can index content and use link popularity to rank content in addition to the basic keyword matches. Enterprise Search is different. Sometimes it requires specially designed indexes as well as several processing steps.
At the U.S. Patent & Trademark Office, part of the Department of Commerce, a team of professionals is building the next generation of search tools using open source technologies. Like any large undertaking, it’s not a simple plug and play project.
Main topics to be covered in this talk:
+ Architectures for Large Scale Enterprise Search
+ Leveraging Apache Cassandra & Spark
+ Customizing / Configuring Apache SolR and Indexing
+ Writing a custom Parser for SolR in Scala
Log Analytics with Amazon Elasticsearch Service - September Webinar SeriesAmazon Web Services
Elasticsearch is a popular open-source search and analytics engine used for log analytics. With Amazon Elasticsearch Service, you can easily run Elasticsearch on AWS. In this webinar, we will provide an overview of Amazon Elasticsearch Service and demo how to set up and configure an Amazon Elasticsearch domain for the log analytics use case.
Learning Objectives:
'- Understand Amazon Elasticsearch Service use cases and key features
- Learn how to secure your Amazon Elasticsearch cluster for access from Kibana and other plug-ins
- Learn best practices for scaling, monitoring, and troubleshooting Amazon Elasticsearch domains
A Retrospective Study to Investigate Association among Age, BMI and BMD in th...IOSR Journals
Bone strength (and, hence, fracture risk) is dependent on many qualities of bone, of which bone mineral density (BMD) is the most commonly measured. Association between advancing age and lower body mass index (BMI) is an important risk factor in the occurrence of low BMD. This study was aimed at evaluation of the association among age, BMI and status of BMD among 159 age matched postmenopausal women who underwent Dual-Energy X-ray Absorptimetry (DEXA) scan. The study population was divided into three groups on the basis of body mass index (BMI) as normal weight, obese and severely obese. The mean bone mineral density (BMD) of obese and severely obese postmenopausal women was found to be significantly higher (P value < 0.001) as compared to the mean BMD of normal weight women. Significant negative correlation was found between the age and BMI except in severely obese group (P value < 0.05). Age and BMD in all the three groups correlated negatively (P value < 0.01) in all the three groups. BMD and BMI in the normal weight group significantly correlated negatively (P value < 0.05) while a very weak positive but insignificant correlation existed between the same in the obese and severely obese postmenopausal women. The study revealed that with advancing age BMD is lowered and that higher BMI might have a positive influence (although not significant as observed in the present study) on the BMD. Other factors like exposure to sunlight, calcium intake, diet etc should also be investigated which could not be probed in the present study as it was a retrospective analysis.
Identifying novel and druggable targets in a triple negative breast cancer ce...Thermo Fisher Scientific
In this study, we developed a CRISPR/Cas9-based high throughput loss-of-function screen for identifying target genes responsible for the tumor proliferation and growth in TNBC. Our initial focus was to identify essential kinases in MDA-MB-231 cell line using the Invitrogen™ LentiArray™ Human Kinase CRISPR Library, which targets 840 kinases with up to 4 different gRNAs per protein kinase for complete gene knockout. This functional screen identified over 90 protein kinases that are essential for cell viability and cell proliferation. Ten of these hits (CDK1, CDK2, CDK8, CDK10, CDK11A, CDK19, CDK19, CDC7, EPHA2 and WEE1) are well-known targets validated in the literature. Currently, we are in the process validating the novel hits through target gene sequencing, western blotting and target specific small molecule kinase inhibitors.
Interrogating differences in expression of targeted gene sets to predict brea...Enrique Moreno Gonzalez
Genomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development of a prognostic test.
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...IJTET Journal
inAbstract— Pattern Recognition (PR) plays an important role in field of Bioinformatics. PR is concerned with processing raw measurement data by a computer to arrive at a prediction that can be used to formulate a decision to be taken. The important problem in which pattern recognition are applied have common that they are too complex to model explicitly. Diverse methods of this PR are used to analyze, segment and manage the high dimensional microarray gene data for classification. PR is concerned with the development of systems that learn to solve a given problem using a set of instances, each instances represented by a number of features. The microarray expression technologies are possible to monitor the expression levels of thousands of genes simultaneously. The microarrays generated large amount of data has stimulate the development of various computational methods to different biological processes by gene expression profiling. Microarray Gene Expression Profiling (MGEP) is important in Bioinformatics, it yield various high dimensional data used in various clinical applications like cancer diagnostics and drug designing. In this work a new schema has developed for classification of unknown malignant tumors into known class. According to this work an new classification scheme includes the transformation of very high dimensional microarray data into mahalanobis space before classification. The eligibility of the proposed classification scheme has proved to 10 commonly available cancer gene datasets, this contains both the binary and multiclass data sets. To improve the performance of the classification gene selection method is applied to the datasets as a preprocessing and data extraction step.
Vassili Soumelis - Programme d’analyse globale et intégrative du micro-enviro...SiRIC_Curie
Programme d’analyse globale et intégrative du
micro-environnement tumoral - Vassili SOUMELIS, MD, PhD
Laboratoire d’Immunologie Clinique et Inserm U932
A CLASSIFICATION MODEL ON TUMOR CANCER DISEASE BASED MUTUAL INFORMATION AND F...Kiogyf
A CLASSIFICATION MODEL ON TUMOR CANCER DISEASE BASED MUTUAL INFORMATION AND FIREFLY ALGORITHM
ABSTRACT
Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches.
Keywords: Feature Selection, Firefly Algorithm, Cancer Disease, Mutual Information
Now a day’s, pharma research is facing challenges in
deciphering molecular understanding of disease initiation,
progress and establishment as well as performance
assessment of drug molecule on such phases of disease
development. Emerging of next generation sequencing
bases molecular tools were found to be a key method for
creating genome wide genomics landscape of gene
mutations, gene expression and gene regulation events.
Although NGS is a powerful tool for molecular research but
same time it have its own technical challenges. Few major
challenges of NGS based pharmacogenomics is
summarized below
1. An Integrated Approach to the Proteomic and Genomic Analysis of Breast Cancer
Using a Cytometric Readout
Keith Shults1
Amanda Chargin1
and Bruce K Patterson2
1 Penfold Patterson Research Institute Menlo Park, CA
2 IncellDx, Inc. Menlo Park, CA
Abstract: The most common approach in today’s world when faced with a complex question is
to apply the latest technology, most recently massively parallel sequencing. There has been an
explosion in genomic technology advancements which focus on one area of cellular detection
for cancer whether it be mRNA expression, DNA copy number analysis, or sequencing. In
contrast to this single minded approach, many years of research and a large literature base
suggests the disease of breast cancer is a multi-factorial system in which proteomic, genomic
and cell cycle alterations are implicated. In addition, it has been reported that approximately
20% of current HER2/ER/PR testing by immunohistochemistry or in-situ hybridization is
inaccurate. This inaccuracy drives a current need for the use of a better integrated approach for
breast cancer diagnostics using an all-encompassing platform with the ability to integrate
proteomic and genomic analysis of breast cells. This integrated platform may be applied to the
fields of therapeutic development as well as basic cellular research.
We wish to share our recent work utilizing an FNA(fine needle aspirate) mimic composed of
mixtures of WBCs, MCF-7, and SK-BR-3 cells that differ in multiple proteins, mRNA expression of
HER2, and genomic integrity (DNA indices and cell cycle alterations). This mimic served as our
model system for the development of our cytometric readout. Feasibility testing of this assay
was initiated through an IRB approved tissue repository designed to collect 40 breast tumors
during the normal course of treatment of breast cancer and 10 normal tissues obtained during
breast reduction procedures. To determine optimal the optimal fixation solution for this assay,
tissues were fixed in 2 different fixatives to emulate how this test may be used in the clinical
setting. We wish to share the clinical performance of our approach and how it relates to the
model system. In addition to the clinical application, the cell based nature of the assay provides
multiple avenues for future application in xenograft models and possibly circulating tumor cells.