[HTML][HTML] On the road to explainable AI in drug-drug interactions prediction: A systematic review

TH Vo, NTK Nguyen, QH Kha, NQK Le - Computational and Structural …, 2022 - Elsevier
Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …

A novel graph mining approach to predict and evaluate food-drug interactions

MM Rahman, SM Vadrev, A Magana-Mora, J Levman… - Scientific reports, 2022 - nature.com
Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates
biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a …

MECDDI: clarified drug–drug interaction mechanism facilitating rational drug use and potential drug–drug interaction prediction

W Hu, W Zhang, Y Zhou, Y Luo, X Sun… - Journal of Chemical …, 2023 - ACS Publications
Drug–drug interactions (DDIs) are a major concern in clinical practice and have been
recognized as one of the key threats to public health. To address such a critical threat, many …

BDN-DDI: A bilinear dual-view representation learning framework for drug–drug interaction prediction

G Ning, Y Sun, J Ling, J Chen, J He - Computers in Biology and Medicine, 2023 - Elsevier
Drug–drug interactions (DDIs) refer to the potential effects of two or more drugs interacting
with each other when used simultaneously, which may lead to adverse reactions or reduced …

DCGG: drug combination prediction using GNN and GAE

SS Ziaee, H Rahmani, M Tabatabaei, AHC Vlot… - Progress in Artificial …, 2024 - Springer
Recent findings show that drug combination therapy can increase efficacy, decrease drug
resistance, and reduce drug side effects. Due to the enormous number of possibilities in the …

[HTML][HTML] A knowledge-integrated deep learning framework for cellular image analysis in parasite microbiology

R Feng, S Li, Y Zhang - STAR protocols, 2023 - Elsevier
Cellular image analysis is an important method for microbiologists to identify and study
microbes. Here, we present a knowledge-integrated deep learning framework for cellular …

A tandem white shark algorithm approach for optimizing drug–disease and drug–drug interactions in multimorbidity and polypharmacy

SM Al Khatib - Biomedical Signal Processing and Control, 2024 - Elsevier
Minimizing Drug–disease and drug–drug Interactions in Polypharmacy and Multimorbidity
(DIPM) is a challenging problem. The challenge is caused by the large number of similar …

DDREL: From drug-drug relationships to drug repurposing

M Allahgholi, H Rahmani, D Javdani… - Intelligent Data …, 2022 - content.iospress.com
Analyzing the relationships among various drugs is an essential issue in the field of
computational biology. Different kinds of informative knowledge, such as drug repurposing …

DcDiRNeSa, Drug Combination Prediction by Integrating Dimension Reduction and Negative Sampling Techniques

M Tabatabaei, H Rahmani… - Journal of AI and Data …, 2023 - jad.shahroodut.ac.ir
The search for effective treatments for complex diseases, while minimizing toxicity and side
effects, has become crucial. However, identifying synergistic combinations of drugs is often a …

Genre Classification of Movies from a Single Poster Image Using Feature Fusion

F Nadem, R Mahdian, H Zareian - 2021 7th International …, 2021 - ieeexplore.ieee.org
The movie industry is one of the largest and most influential sectors of any community. Each
movie in the industry consists of different elements such as actors, directors, preparation …