A Self-Supervised Learning Framework for Classifying Microarray Gene Expression Data

dc.contributor.authorLu, Yijuan
dc.contributor.authorTian, Qi
dc.contributor.authorLiu, Feng
dc.contributor.authorSanchez, Maribel
dc.contributor.authorWang, Yufeng
dc.date.accessioned2023-10-23T14:49:50Z
dc.date.available2023-10-23T14:49:50Z
dc.date.issued2006-10
dc.description.abstractIt is important to develop computational methods that can effectively resolve two intrinsic problems in microarray data: high dimensionality and small sample size. In this paper, we propose a self-supervised learning framework for classifying microarray gene expression data using Kernel Discriminant-EM (KDEM) algorithm. This framework applies self-supervised learning techniques in an optimal nonlinear discriminating subspace. It efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends linear algorithm in DEM to kernel algorithm to handle nonlinearly separable data in a lower dimensional space. Extensive experiments on the Plasmodium falciparum expression profiles show the promising performance of the approach.
dc.description.departmentComputer Science
dc.description.sponsorshipThis work is supported in part by San Antonio Life Science Institute (SALSI) and ARO grant W911NF-05-1-0404 to Q. Tian, and San Antonio Area Foundation, NIH RCMI grant 2G12RR013646-06A1, and UTSA Faculty Research Award to Y. Wang.
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2121
dc.language.isoen_US
dc.publisherUTSA Department of Computer Science
dc.relation.ispartofseriesTechnical Report; CS-TR-2006-010
dc.titleA Self-Supervised Learning Framework for Classifying Microarray Gene Expression Data
dc.typeTechnical Report

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