NDLI: Conditional Random Field for Candidate Gene Prioritization

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Conditional Random Field for Candidate Gene Prioritization

Content Provider	ACM Digital Library
Author	Gilliam, Conrad Maltsev, Natalia Xie, Bingqing Agam, Gady
Abstract	Prioritization of novel disease genes is a major challenge in bioinformatics. The large amount of data collected from modern biological experiments makes it difficult for biologists to determine how information on a particular gene relates to a disease or phenotype, whereas performing exhaustive experiments on all possible combinations is impossible. Computational approaches are thus crucial in automating the process of extracting critical annotation and patterns and predicting relevant novel genes with high confidence. In this paper we propose a new method for prioritizing disease genes using both annotations on the genes as well as the underlying gene interaction network. Our approach is unique in that it uses a conditional random field to simultaneously exploit both network and annotation information directly without attempting to convert the network information into features or vice versa. Performance evaluation on standard data sets achieves a median ranking of 29% and over 0.6 area under curve value in cross-validation experiments on 42 diseases.
Starting Page	700
Ending Page	701
Page Count	2
File Format	PDF
ISBN	9781450324342
DOI	10.1145/2506583.2512374
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-09-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Conditional random field Sparse feature spaces Combined features and network Gene prioritization Regularization Belief propagation
Content Type	Text
Resource Type	Article

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