Name
Title | ||
|---|---|---|
GBO-kNN a new framework for enhancing the performance of ligand-based virtual screening for drug discovery |
Abstract | ||
|---|---|---|
•GBO-kNN is a proposed framework based on a wrapper approach for features selection.•The aim of the research is to reach maximum classification accuracy.•The performance of GBO-kNN is evaluated against real benchmark datasets.•The GBO-kNN performance is compared against seven recent metaheuristic algorithms.•Results showed high effectiveness on one dataset and moderate on another. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.eswa.2022.116723 | Expert Systems with Applications |
Keywords | DocType | Volume |
Virtual screening (VS),Classification accuracy,Feature selection,Gradient-Based Optimizer (GBO),k-Nearest Neighbors (k-NN) | Journal | 197 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aya A. Mostafa | 1 | 0 | 0.34 |
| Amr A. Alhossary | 2 | 0 | 0.34 |
| Sameh A. Salem | 3 | 0 | 0.34 |
| Amr E. Mohamed | 4 | 2 | 1.84 |