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 |
|
| Machine Learning | ICML | Helsinki, Finland | -- |
2/8 |
7/5-9 |
| COLT | Helsinki, Finland | -- |
2/20 |
7/9-12 |
|
| AAAI | Chicago, Illinois | 2/25 |
2/30 |
7/13-17 |
|
| KDD | Las Vegas, Nevada | 2/23 |
2/29 |
8/24-27 |
|
| UAI | Helsinki, Finland | 2/23 |
2/29 |
7/9-12 |
|
| RECOMB | Singapore | -- |
-- |
3/30-4/2 |
|
| Bioinformatics | ISMB | Toronto, Canada | 1/16 |
7/19-23 |
|
| Biophysical | Long Beach, California | -- |
-- |
2/2-6 |
|
| PSB | Big Island of Hawaii | -- |
-- |
1/4-8 |
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.