[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …
process of drug discovery. There is a need to develop novel and efficient prediction …
Machine learning for drug-target interaction prediction
Identifying drug-target interactions will greatly narrow down the scope of search of candidate
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …
the drug discovery process. It narrows down the search space for interactions by suggesting …
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …
development. Computational prediction of DTIs can effectively complement experimental …
[HTML][HTML] A comprehensive review of feature based methods for drug target interaction prediction
Drug target interaction is a prominent research area in the field of drug discovery. It refers to
the recognition of interactions between chemical compounds and the protein targets in the …
the recognition of interactions between chemical compounds and the protein targets in the …
SperoPredictor: an integrated machine learning and molecular docking-based drug repurposing framework with use case of COVID-19
F Ahmed, JW Lee, A Samantasinghar, YS Kim… - Frontiers in public …, 2022 - frontiersin.org
The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human
hosts as a contagious disease, and its variants have induced a pandemic resulting in the …
hosts as a contagious disease, and its variants have induced a pandemic resulting in the …
Comparison study of computational prediction tools for drug-target binding affinities
The drug development is generally arduous, costly, and success rates are low. Thus, the
identification of drug-target interactions (DTIs) has become a crucial step in early stages of …
identification of drug-target interactions (DTIs) has become a crucial step in early stages of …
ADMET evaluation in drug discovery. 19. Reliable prediction of human cytochrome P450 inhibition using artificial intelligence approaches
Adverse effects induced by drug–drug interactions may result in early termination of drug
development or even withdrawal of drugs from the market, and many drug–drug interactions …
development or even withdrawal of drugs from the market, and many drug–drug interactions …
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method
Y Chu, X Shan, T Chen, M Jiang, Y Wang… - Briefings in …, 2021 - academic.oup.com
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug
repositioning. To reduce the experimental cost, a large number of computational …
repositioning. To reduce the experimental cost, a large number of computational …