NDLI: Finding Informative Genes from Multiple Microarray Experiments: A Graph-based Consensus Maximization Model

Content Provider	IEEE Xplore Digital Library
Author	Liang Ge Nan Du Aidong Zhang
Copyright Year	2011
Abstract	With the rapid advancement of biology technology, many micro array experiments are conducted towards the same problem of finding informative genes. Therefore, it is important to find a set of informative genes integrating multiple micro array experiments that achieves maximal consensus. Most previous researches formulated this problem as a rank aggregation problem. In this paper, we propose a novel Graph-based Consensus Maximization (GCM) model to estimate the conditional probability of each gene being informative, then the genes are ranked by this probability. The estimation of the probabilities is formulated as an optimization problem on a bipartite graph, where the criterion function favors the smoothness of the prediction over the graph and penalizes deviations from the initial input ranked lists from micro array experiments. We solve this problem through iterative propagation of probability estimates among neighboring nodes. In addition, when certain genes have already been identified to be informative, it has never been explored in the literature how to take advantage of such information to improve the consensus result. Our proposed GCM model can be naturally extended to incorporate such information, thus increasing the quality of the predicted result. In the experimental evaluation, we conducted experiments on the five prostate cancer micro array studies. The results showed that our model outperformed other baseline methods in finding informative genes. Furthermore, by adding only one piece of information that some gene is informative, our model yielded a significantly better result. The experimental evaluation demonstrates that the proposed GCM model is effective and superior in finding informative genes from multiple micro array experiments.
Starting Page	506
Ending Page	511
File Size	213515
Page Count	6
File Format	PDF
ISBN	9781457717994
DOI	10.1109/BIBM.2011.34
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Markov processes Bipartite graph Mathematical model Prostate cancer Optimization Equations
Content Type	Text
Resource Type	Article

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