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CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
16 years 1 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
FLAIRS
2008
15 years 9 months ago
Learning a Probabilistic Model of Event Sequences from Internet Weblog Stories
One of the central problems in building broad-coverage story understanding systems is generating expectations about event sequences, i.e. predicting what happens next given some a...
Mehdi Manshadi, Reid Swanson, Andrew S. Gordon
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
2007
133views more  BMCBI 2007»
15 years 7 months ago
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. ...
Ivan G. Costa, Roland Krause, Lennart Opitz, Alexa...
TMC
2008
78views more  TMC 2008»
15 years 7 months ago
SELECT: Self-Learning Collision Avoidance for Wireless Networks
The limited number of orthogonal channels and autonomous installations of hotspots and home wireless networks often leave neighboring 802.11 basic service sets (BSSs) operating on ...
Chun-cheng Chen, Eunsoo Seo, Hwangnam Kim, Haiyun ...