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» On Learning with Integral Operators
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CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
16 years 12 days 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
198
Voted
ICWE
2003
Springer
16 years 47 min ago
Semi-automatic Assessment Process in a Ubiquitous Environment for Language Learning
This work presents a research on the methods and mechanisms necessary to bring the Information and Communication Technologies in the traditional classroom. This will be achieved by...
Maximiliano Paredes, Manuel Ortega, Pedro P. S&aac...
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 6 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...