Machine Learning in Biology
This site is devoted to machine learning in bioinformatics, thus far, specializing in proteomics in terms of both protein mutation, function and structure prediction.
Featured: Protein-DNA Interactions
Transcription regulation is a fundamental biological process, and expansive efforts have been made to investigate its mechanisms through both biological experiments and computational modeling based on physical-chemical principles. This data is subsequently used to construct regulation networks in order to investigate the underlying gene expression in the cell. Cont.
Upcoming Conferences
| Conference | Location | Abstract |
Paper |
Starts |
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| Machine Learning | ICML | Montreal, Canada | -- |
1/26 |
6/14 |
| COLT | Montreal, Canada | -- |
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| AAAI-2010 | Atlanta, Georgia | 7/11 |
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| KDD | Paris | 2/2 |
2/6 |
6/28 |
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| RECOMB | Tucson, Arizona | -- |
10/6 |
5/18 |
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| Bioinformatics | ISMB | Stockholm, Sweden | 1/16 |
7/19-23 |
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| PSB | Big Island of Hawaii | -- |
1/5 |
Research Highlight (NIPS 2007)
AdaBoost has proven an effective algorithm in many domains. However, a majority of current research for large-scale problems focus on linear or kernel machines. The FilterBoost algorithm is a theoretically motivated adaptive boosting algorithm that works in the filtering framework.