iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting
Prediction of new drug-target interactions is extremely important as it can lead the researchers to find new uses for old drugs and to realize the therapeutic profiles or side effects thereof. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for prediction of new drug-target interactions have gained much interest in recent times. We present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel balancing technique and a boosting technique for the binary classification problem of drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under Receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method in the literature to-date in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued to be more appropriate as a metric for comparison for imbalanced datasets, like the one studied in this research. In the sequel, our experiments establish the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is readily available for use at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/.
PSSM bigram => A
pssm_to_bigram_generator.py file was used to create the feature set A
Accessible Surface Area Composition + Secondary Structure Composition + Torsional Angles Composition => B
creating_structural_info.py file was used to create the feature set B
Torsional Angles Auto-Covariance + Structural Probabilities Auto-Covariance => C
spider_to_auto_covar_generator.py file was used to create the feature set C
Torsional Angles bigram + Structural Probabilities bigram => D
spider_to_bigram_generator.py file was used to create the feature set D
Use the codes named
cluster_based_under_sampling.py
and
random_under_sampling.py
for experimenting a dataset with a classifier.