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BMCBI
2010
179views more  BMCBI 2010»
15 years 7 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...
BMCBI
2010
98views more  BMCBI 2010»
15 years 7 months ago
Learning to predict expression efficacy of vectors in recombinant protein production
Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is t...
Wen-Ching Chan, Po-Huang Liang, Yan-Ping Shih, Uen...
BMCBI
2010
147views more  BMCBI 2010»
15 years 7 months ago
Learning biological network using mutual information and conditional independence
Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a revers...
Dong-Chul Kim, Xiaoyu Wang, Chin-Rang Yang, Jean G...
BMCBI
2006
151views more  BMCBI 2006»
15 years 7 months ago
Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues
Background: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information ret...
Hua Xu, Marianthi Markatou, Rositsa Dimova, Hongfa...
JMLR
2006
132views more  JMLR 2006»
15 years 7 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof