Name
Abstract | ||
|---|---|---|
In this paper, we present a novel method for predicting gene functional interactions. We study the effectiveness of various raw and derived features from neural word embedding learned from biomedical literature. Our evaluation results demonstrate that the information captured in neural word embedding is very useful and our learned classification models are capable of predicting gene functional interactions with high accuracy. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICTAI.2016.0084 | 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | Field | DocType |
gene functional interaction,semantic word embedding,classification,biomedical | Pattern recognition,Computer science,Natural language processing,Artificial intelligence,Word embedding,Artificial neural network,Vocabulary,Semantics,Machine learning | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-5090-4460-3 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| arpita roy | 1 | 14 | 4.39 |
| Shimei Pan | 2 | 684 | 64.41 |