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BIBM
2007
IEEE
135views Bioinformatics» more  BIBM 2007»
13 years 11 months ago
Graph Kernel-Based Learning for Gene Function Prediction from Gene Interaction Network
Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer ge...
Xin Li, Zhu Zhang, Hsinchun Chen, Jiexun Li
BMCBI
2010
179views more  BMCBI 2010»
13 years 4 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...
CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
13 years 10 months ago
Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Xutao Deng, Huimin Geng, Hesham H. Ali
BMCBI
2008
111views more  BMCBI 2008»
13 years 4 months ago
Information-based methods for predicting gene function from systematic gene knock-downs
Background: The rapid annotation of genes on a genome-wide scale is now possible for several organisms using high-throughput RNA interference assays to knock down the expression o...
Matthew T. Weirauch, Christopher K. Wong, Alexandr...
BMCBI
2008
160views more  BMCBI 2008»
13 years 4 months ago
Predicting cancer involvement of genes from heterogeneous data
Background: Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characte...
Ramon Aragues, Chris Sander, Baldo Oliva