Name
Abstract | ||
|---|---|---|
This paper is focused on determining which proteins affect the activity of Aryl Hydrocarbon Receptor (AHR) system when learning a model that can accurately predict its activity when single genes are knocked out. Experiments with results are presented when models are trained on a single source of information: abstracts from Medline (http://medline.cos.com/) that talk about the genes involved in the experiments. The results suggest that AdaBoost classifier with a binary bag-of-words representation obtains significantly better results. |
Year | Venue | Keywords |
|---|---|---|
2008 | BioNLP | functional property,single source,better result,single gene,aryl hydrocarbon receptor,adaboost classifier,textual information,binary bag-of-words representation |
Field | DocType | Citations |
Gene,Computer science,Textual information,Artificial intelligence,Aryl hydrocarbon receptor,MEDLINE,Adaboost classifier,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.36 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oana Frunza | 1 | 75 | 7.02 |
| Diana Inkpen | 2 | 1059 | 87.92 |