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IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Saini,A, Hou,J and Zhou,W 2014, IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples, in Biotech 2014 : Innovative Approach in Stem Cell Research, Cancer Biology and Applied Biotechnology, Excellent Publishing House, Kishangarh, New Delhi, pp. 13-30.

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Title IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples
Author(s) Saini,A
Hou,JORCID iD for Hou,J orcid.org/0000-0002-6403-9786
Zhou,WORCID iD for Zhou,W orcid.org/0000-0002-1680-2521
Conference name Stem Cell Research, Cancer Biology and Applied Biotechnology. World Congress (2014 : New Delhi, India)
Conference location New Delhi, India
Conference dates 2014/5/3 - 2014/5/3
Title of proceedings Biotech 2014 : Innovative Approach in Stem Cell Research, Cancer Biology and Applied Biotechnology
Editor(s) Johri,AK
Mishra,GC
Publication date 2014
Conference series Stem Cell Research, Cancer Biology and Applied Biotechnology World Congress
Start page 13
End page 30
Total pages 18
Publisher Excellent Publishing House
Place of publication Kishangarh, New Delhi
Keyword(s) estrogen receptor
pathologic complete re sponse
chemotherapy
gene expression
prediction
network
Summary Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.
ISBN 9789383083794
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080299 Computation Theory and Mathematics not elsewhere classified
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Excellent Publishing House
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069457

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.