Name
Title | ||
|---|---|---|
Effectively identifying compound-protein interactions by learning from positive and unlabeled examples. |
Abstract | ||
|---|---|---|
Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing mach... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCBB.2016.2570211 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | Field | DocType |
Proteins,Compounds,Drugs,Support vector machines,Learning systems,Training data,Machine learning | Tensor product,PU learning,Computer science,Artificial intelligence,Predictive modelling,Random forest,k-nearest neighbors algorithm,Feature vector,Naive Bayes classifier,Pattern recognition,Support vector machine,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 6 | 1545-5963 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhanzhan Cheng | 1 | 18 | 5.55 |
| Shuigeng Zhou | 2 | 2089 | 207.00 |
| Wang Yang | 3 | 1 | 0.35 |
| Hui Liu | 4 | 15 | 2.30 |
| Jihong Guan | 5 | 657 | 81.13 |
| Yi-ping Phoebe Chen | 6 | 1060 | 128.42 |