Author:
Bonet Isis,García María M.,Saeys Yvan,Van de Peer Yves,Grau Ricardo
Publisher
Springer Berlin Heidelberg
Reference17 articles.
1. Sevin, A.D., DeGruttola, V., Nijhuis, M., Schapiro, J.M., Foulkes, A.S., Para, M.F., Boucher, C.A.B.: Methods for investigation of the relationship between drug-susceptibility phenotype and human immunodeficiency virus type 1 genotype with applications to aids clinical trials group 333. Journal Of Infectious Diseases 182(1), 59–67 (2000)
2. Scmidt, B., Walter, H., Moschik, B.: Simple algorithm derived from ageno-/phenotypic database to predict HIV-1 protease inhibitor resistance. AIDS 14, 1731–1738 (2000)
3. Wang, D.C., Larder, B.: Enhanced prediction of lopinavir resistance from genotype by use of artificial neural networks. Journal Of Infectious Diseases 188(5), 653–660 (2003)
4. Beerenwinkel, N., Schmidt, B., Walter, H., Kaiser, R., Lengauer, T., Hoffmann, D., Korn, K., Selbig, J.: Diversity and complexity of hiv-1 drug resistance: A bioinformatics approach to predicting phenotype from genotype. PNAS 99(12), 8271–8276 (2002)
5. James, R.: Predicting Human Immunodeficiency Virus Type 1 Drug Resistance from Genotype Using Machine Learning. Msc thesis, University of Edinburgh (2004)
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Multi-conformation Aproach of ENM-NMA Dynamic-Based Descriptors for HIV Drug Resistance Prediction;Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications;2023-11-27
2. Neural Drug Discovery;2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo);2022-11-21
3. Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level;Molecular Diversity;2021-07-09
4. A renaissance of neural networks in drug discovery;Expert Opinion on Drug Discovery;2016-07-04
5. Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps;Advances in Computational Intelligence;2013