NDLI: Finding informative genes for prostate cancer: a general framework of integrating heterogeneous sources

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Finding informative genes for prostate cancer: a general framework of integrating heterogeneous sources

Content Provider	ACM Digital Library
Author	Du, Nan Ge, Liang Gao, Jing Zhang, Aidong
Abstract	Finding informative genes for prostate cancer has always been an important topic in cancer study. With the widespread use of genomic analysis and microarray experiments, a large number of genes can be analyzed efficiently to find the informative ones based on high-throughput microarray experiments [5--9]. On the other hand, based on clinical studies, several genes have already been identified to be important in prostate cancer development and progression [23--28]. These research results come from heterogeneous sources, with different formats, and expressing different perspectives of the problem of finding informative genes for prostate cancer. In this work, we are aiming to find the informative genes for prostate cancer by utilizing these heterogeneous sources of information from various research progresses. We propose a general framework that encodes various heterogeneous sources including ranked lists of informative genes [5--9], microarray expression data [5--9] and important genes identified by [23--28]. The proposed framework estimates the conditional probability of a gene being informative and ranks the genes by this probability. The estimation of such probability is formulated as an optimization problem, where we propose an efficient iterative algorithm to solve the optimization problem. Furthermore, we show that the problem formulation is convex and the iterative algorithm converges to the global optimal value. Extensive experiments show that the utilization of heterogeneous information is very helpful in finding informative genes and the proposed method outperforms many other baseline methods.
Starting Page	274
Ending Page	281
Page Count	8
File Format	PDF
ISBN	9781450316705
DOI	10.1145/2382936.2382971
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-07
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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