Graph representation learning in bioinformatics: trends, methods and applications
Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
[HTML][HTML] On the road to explainable AI in drug-drug interactions prediction: A systematic review
Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
Attention-based knowledge graph representation learning for predicting drug-drug interactions
Drug–drug interactions (DDIs) are known as the main cause of life-threatening adverse
events, and their identification is a key task in drug development. Existing computational …
events, and their identification is a key task in drug development. Existing computational …
MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism
One of the main problems with the joint use of multiple drugs is that it may cause adverse
drug interactions and side effects that damage the body. Therefore, it is important to predict …
drug interactions and side effects that damage the body. Therefore, it is important to predict …
SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization
Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …
[HTML][HTML] Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in early drug discovery
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical
industry and research, where it has been utilized to efficiently identify new chemical entities …
industry and research, where it has been utilized to efficiently identify new chemical entities …
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
Drug discovery and development is a complex and costly process. Machine learning
approaches are being investigated to help improve the effectiveness and speed of multiple …
approaches are being investigated to help improve the effectiveness and speed of multiple …
Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study
L Murali, G Gopakumar, DM Viswanathan… - Journal of biomedical …, 2023 - Elsevier
With the growth of data and intelligent technologies, the healthcare sector opened numerous
technology that enabled services for patients, clinicians, and researchers. One major hurdle …
technology that enabled services for patients, clinicians, and researchers. One major hurdle …
SMR: medical knowledge graph embedding for safe medicine recommendation
Most of the existing medicine recommendation systems that are mainly based on electronic
medical records (EMRs) are significantly assisting doctors to make better clinical decisions …
medical records (EMRs) are significantly assisting doctors to make better clinical decisions …
[HTML][HTML] Understanding the performance of knowledge graph embeddings in drug discovery
Abstract Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE)
models have recently begun to be explored in the context of drug discovery and have the …
models have recently begun to be explored in the context of drug discovery and have the …