Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …
learning models have established their usefulness in biomedical applications, especially in …
[HTML][HTML] Construction of knowledge graphs: Current state and challenges
With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …
systems and question-answering, the need for generalized pipelines to construct and …
KG-Predict: A knowledge graph computational framework for drug repurposing
The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data
has offered unprecedented opportunities for drug discovery including drug repurposing …
has offered unprecedented opportunities for drug discovery including drug repurposing …
SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs
Abstract piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment
as novel biomarkers due to its abnormal expression in various cancers. However, the …
as novel biomarkers due to its abnormal expression in various cancers. However, the …
A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks
Drug–drug interactions (DDIs) prediction is a challenging task in drug development and
clinical application. Due to the extremely large complete set of all possible DDIs, computer …
clinical application. Due to the extremely large complete set of all possible DDIs, computer …
Ichrom-deep: an attention-based deep learning model for identifying chromatin interactions
P Zhang, H Wu - IEEE Journal of Biomedical and Health …, 2023 - ieeexplore.ieee.org
Identification of chromatin interactions is crucial for advancing our knowledge of gene
regulation. However, due to the limitations of high-throughput experimental techniques …
regulation. However, due to the limitations of high-throughput experimental techniques …
A geometric deep learning framework for drug repositioning over heterogeneous information networks
Drug repositioning (DR) is a promising strategy to discover new indicators of approved
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
Predicting drug-target interactions over heterogeneous information network
Identifying Drug-Target Interactions (DTIs) is a critical step in studying pathogenesis and
drug development. Due to the fact that conventional experimental methods usually suffer …
drug development. Due to the fact that conventional experimental methods usually suffer …
AMDECDA: attention mechanism combined with data ensemble strategy for predicting CircRNA-disease association
Accumulating evidence from recent research reveals that circRNA is tightly bound to human
complex disease and plays an important regulatory role in disease progression. Identifying …
complex disease and plays an important regulatory role in disease progression. Identifying …
A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions
M Ma, X Lei - PLOS Computational Biology, 2023 - journals.plos.org
Expressive molecular representation plays critical roles in researching drug design, while
effective methods are beneficial to learning molecular representations and solving related …
effective methods are beneficial to learning molecular representations and solving related …