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. Attached Files Name Description MIMEType Size Downloads saini-ipaintegrated-2014.pdf Published version application/pdf 549.74KB 86 Title IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples Author(s) Saini,A Hou,J orcid.org/0000-0002-6403-9786 Zhou,W 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30069457 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Publisher URL Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 06 Feb 2015, 11:26:37 EST Fri, 06 Feb 2015, 11:27:38 EST Sat, 07 Feb 2015, 04:00:53 EST Mon, 16 Mar 2015, 11:29:26 EST Wed, 08 Apr 2015, 16:10:01 EST Thu, 09 Apr 2015, 06:07:19 EST Thu, 30 Apr 2015, 08:49:06 EST Tue, 05 May 2015, 16:00:46 EST Tue, 05 May 2015, 16:01:29 EST Fri, 08 May 2015, 12:31:33 EST Tue, 02 Jun 2015, 15:51:12 EST Wed, 03 Jun 2015, 11:15:30 EST Wed, 07 Sep 2016, 08:22:06 EST Wed, 06 Dec 2017, 06:12:34 EST Fri, 07 Sep 2018, 17:24:49 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 616 Abstract Views, 89 File Downloads  -  Detailed Statistics Created: Mon, 16 Mar 2015, 11:29:26 EST