Name
Title | ||
|---|---|---|
A dissimilarity-based multiple instance learning approach for protein remote homology detection |
Abstract | ||
|---|---|---|
•We address Protein Remote Homology Detection with a Multiple Instance Learning method.•Multiple Instance Learning has never been used in this context.•Multiple Instance Learning permits to use longer fragments (biologically relevant).•Experiments confirm the suitability of the approach.•Proposed solution well compares with State of the Art. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patrec.2019.08.027 | Pattern Recognition Letters |
Keywords | Field | DocType |
Protein Remote Homology Detection,Multiple-instance learning,Dissimilarity representation | Pattern recognition,Homology (biology),Artificial intelligence,Mathematics,Binary number | Journal |
Volume | ISSN | Citations |
128 | 0167-8655 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonella Mensi | 1 | 3 | 1.39 |
| Manuele Bicego | 2 | 1028 | 72.30 |
| Pietro Lovato | 3 | 126 | 9.80 |
| Marco Loog | 4 | 1796 | 154.31 |
| David M. J. Tax | 5 | 2071 | 148.87 |