Name
Abstract | ||
|---|---|---|
•Review of existing ML-based in-silico screening models for drug discovery domain.•Creation of new feature clustering techniques to tackle high-dimensionality datasets.•Use of problem-oriented measures to improve classification accuracy.•Comparison of D2-MCS against most popular ML classifiers in the drug discovery domain.•Construction of ML models according to the intrinsic characteristic of each cluster. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2018.12.032 | Expert Systems with Applications |
Keywords | Field | DocType |
Drug discovery,Machine learning algorithms,Feature clustering,Multiple classifier systems | Data mining,Drug discovery,chEMBL,Voting,Computer science,Curse of dimensionality,Artificial intelligence,Classifier (linguistics),Abstract machine,Machine learning | Journal |
Volume | ISSN | Citations |
121 | 0957-4174 | 0 |
PageRank | References | Authors |
0.34 | 23 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Ruano-Ordás | 1 | 94 | 9.32 |
| Iryna Yevseyeva | 2 | 72 | 14.98 |
| Vítor Basto Fernandes | 3 | 21 | 5.60 |
| José Ramon Méndez | 4 | 254 | 17.69 |
| Michael T. M. Emmerich | 5 | 247 | 22.74 |