期刊论文

Li, Zhoujun

英文

BMC Genomics

1471-2164

2010

11

2

1-9

10.1186/1471-2164-11-S2-S11

This work is supported in part by the National Science Foundation NSF CCF 0905291, NSF IIS 0448023, NSF IIP 0934197, Natural Science Foundation in China with grant No 60573057 ”Research on Key Technology of Data Minging” and grant No 90920005 ”Chinese Language Semantic Knowledge Acquisition and Semantic Computational Model Study”. We would like to thank Prof. Chih-Jen Lin from National Taiwan University for using SVM matlab toolkit. Publication of this supplement was made possible with

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Background: A large amount of functional genomic data have provided enough knowledge in predicting gene function computationally, which uses known functional annotations and relationship between unknown genes and known ones to map unknown genes to GO functional terms. The prediction procedure is usually formulated as binary classification problem. Training binary classifier needs both positive examples and negative ones that have almost the same size. However, from various annotation database, we can only obtain few positive genes annotation fo...