Name
Title | ||
|---|---|---|
Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques. |
Abstract | ||
|---|---|---|
•The study compared the performance of artificial neural network, support vector machine and random forest on predicting anti-tuberculosis drugs induced hepatotoxicity.•The best performance to predict anti-tuberculosis drugs induced hepatotoxicity was generated by artificial neural network among three bio-prospecting techniques.•Combining genomic and clinical data can further increase the area under receiver operating characteristic curve than using genomic or clinical data alone. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.cmpb.2019.105307 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
Tuberculosis,Anti-tuberculosis drugs,Gene polymorphism,Artificial neural network,Support vector machine,Random forest,Feature selection | Journal | 188 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nai-Hua Lai | 1 | 0 | 0.34 |
| Wan-Chen Shen | 2 | 3 | 1.11 |
| Chun-Nin Lee | 3 | 0 | 0.34 |
| Jui-Chia Chang | 4 | 0 | 0.34 |
| Man-Ching Hsu | 5 | 0 | 0.34 |
| Li-Na Kuo | 6 | 6 | 2.03 |
| Ming-Chih Yu | 7 | 0 | 0.34 |
| Hsiang-Yin Chen | 8 | 3 | 1.11 |