PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection …

SMH Mahmud, W Chen, Y Liu, MA Awal… - Briefings in …, 2021 - academic.oup.com
Discovering drug–target (protein) interactions (DTIs) is of great significance for researching
and developing novel drugs, having a tremendous advantage to pharmaceutical industries …

Identification of sub-Golgi protein localization by use of deep representation learning features

Z Lv, P Wang, Q Zou, Q Jiang - Bioinformatics, 2020 - academic.oup.com
Abstract Motivation The Golgi apparatus has a key functional role in protein biosynthesis
within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases …

Prediction of drug-target interactions based on multi-layer network representation learning

Y Shang, L Gao, Q Zou, L Yu - Neurocomputing, 2021 - Elsevier
The prediction of drug-target interactions aims to identify potential targets for the treatment of
new and rare diseases. The large number of unknown combinations between drugs and …

Prediction of protein–protein interactions based on elastic net and deep forest

B Yu, C Chen, X Wang, Z Yu, A Ma, B Liu - Expert Systems with …, 2021 - Elsevier
Prediction of protein–protein interactions (PPIs) helps to grasp molecular roots of disease.
However, web-lab experiments to predict PPIs are limited and costly. Using machine …

A convolutional neural network using dinucleotide one-hot encoder for identifying DNA N6-methyladenine sites in the rice genome

Z Lv, H Ding, L Wang, Q Zou - Neurocomputing, 2021 - Elsevier
Abstract N6-methyladenine (m 6 A) is one of the crucial epigenetic modifications and is
related to the control of various DNA processes. Carrying out a genome-wide m 6 A analysis …

AOPs-SVM: a sequence-based classifier of antioxidant proteins using a support vector machine

C Meng, S Jin, L Wang, F Guo, Q Zou - Frontiers in Bioengineering …, 2019 - frontiersin.org
Antioxidant proteins play important roles in countering oxidative damage in organisms.
Because it is time-consuming and has a high cost, the accurate identification of antioxidant …

Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting

SMH Mahmud, W Chen, H Meng, H Jahan, Y Liu… - Analytical …, 2020 - Elsevier
Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the
drug discovery process, having enormous benefit to the patients and pharmaceutical …

Gradient boosting decision tree-based method for predicting interactions between target genes and drugs

P Xuan, C Sun, T Zhang, Y Ye, T Shen, Y Dong - Frontiers in genetics, 2019 - frontiersin.org
Determining the target genes that interact with drugs—drug–target interactions—plays an
important role in drug discovery. Identification of drug–target interactions through biological …

Identification of drug-side effect association via semisupervised model and multiple kernel learning

Y Ding, J Tang, F Guo - IEEE journal of biomedical and health …, 2018 - ieeexplore.ieee.org
Drug-side effect association contains the information on marketed medicines and their
recorded adverse drug reactions. Traditional experimental method is time consuming and …

iTTCA-RF: a random forest predictor for tumor T cell antigens

S Jiao, Q Zou, H Guo, L Shi - Journal of translational medicine, 2021 - Springer
Background Cancer is one of the most serious diseases threatening human health. Cancer
immunotherapy represents the most promising treatment strategy due to its high efficacy and …