alexa Identification Of Gene Biomarkers For Cancer Tumour Classification Using Cross-validated Area Under The ROC Curve | 68447
ISSN: 2168-9695

Advances in Robotics & Automation
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

3rd International conference on Artificial Intelligence & Robotics

W. B. Yahya
Department of Statistics, University of Ilorin, Nigeria
Posters & Accepted Abstracts: Adv Robot Autom
DOI: 10.4172/2168-9695-C1-009
Abstract
Microarray-based cancer classifications using gene expression signatures has been embraced as a viable alternative to clinical identification and diagnosis of cancer tumours. However, the efficiency of the various gene-based classifiers depends largely on the goodness of the crop of genes selected and employed for tumour prediction. Thus, one of the common challenges in microarray studies is how to select the crop of genes subset that would be highly predictive of the tissue samples and make biological sense. In this study, an efficient primary gene selection (filtering) method that employs the area under the receiver operating characteristic (ROC) curves for feature selection is presented for binary response microarray data. Gene candidates were selected based on their individual univariate predictive strength of the two tumour subgroups as measured by their respective estimated areas under the ROC curves over a 10-fold cross-validation. Results of the hierarchical clustering with complete linkage search and principal component analysis employed on the selected gene signatures showed a good discrimination of the two biological groups based on the expression levels of the selected gene biomarkers via Monte Carlo experiments. The method, when applied on published lung cancer data set, efficiently classified the two subtypes of lung cancer tumours; malignant pleural mesothelioma (MPM) and adenocarcinoma (ADCA) based on the expression profiles of few selected features from the entire 12,533 genes biomarkers that were measured on 181 mRNA samples. It can be concluded that the new feature selection method proposed here is quite efficient at selecting informative gene inputs that can be further employed by any standard machine learning methods for proper classification of mRNA samples into their respective tumour subgroups in any binary response microarray data problem.
Biography

Email: [email protected]

image PDF   |   image HTML
 

Relevant Topics

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2018-19
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri & Aquaculture Journals

Dr. Krish

[email protected]

+1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

 
© 2008- 2018 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
Leave Your Message 24x7