AIMC Topic: Humans

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DRGCL: Drug Repositioning via Semantic-Enriched Graph Contrastive Learning.

IEEE journal of biomedical and health informatics
Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technology and large-scale biological databases, computational drug repositioning has increasingly attracted...

AEGNN-M:A 3D Graph-Spatial Co-Representation Model for Molecular Property Prediction.

IEEE journal of biomedical and health informatics
Improving the drug development process can expedite the introduction of more novel drugs that cater to the demands of precision medicine. Accurately predicting molecular properties remains a fundamental challenge in drug discovery and development. Cu...

Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target Information.

IEEE journal of biomedical and health informatics
Knowledge of unintended effects of drugs is critical in assessing the risk of treatment and in drug repurposing. Although numerous existing studies predict drug-side effect presence, only four of them predict the frequency of the side effects. Unfort...

Prediction of Drug-Target Interactions With High- Quality Negative Samples and a Network-Based Deep Learning Framework.

IEEE journal of biomedical and health informatics
Identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared to traditional experimental methods, computer-based methods for predicting DTIs can significantly reduce the time and financial burdens of drug develop...

MDTL-ACP: Anticancer Peptides Prediction Based on Multi-Domain Transfer Learning.

IEEE journal of biomedical and health informatics
Anticancer peptides (ACPs) have emerged as one of the most promising therapeutic agents for cancer treatment. They are bioactive peptides featuring broad-spectrum activity and low drug-resistance. The discovery of ACPs via traditional biochemical met...

Semantic-Enhanced Graph Contrastive Learning With Adaptive Denoising for Drug Repositioning.

IEEE journal of biomedical and health informatics
The traditional drug development process requires a significant investment in workforce and financial resources. Drug repositioning as an efficient alternative has attracted much attention during the last few years. Despite the wide application and s...

Decoding Drug Response With Structurized Gridding Map-Based Cell Representation.

IEEE journal of biomedical and health informatics
A thorough understanding of cell-line drug response mechanisms is crucial for drug development, repurposing, and resistance reversal. While targeted anticancer therapies have shown promise, not all cancers have well-established biomarkers to stratify...

Enhancing Drug Repositioning Through Local Interactive Learning With Bilinear Attention Networks.

IEEE journal of biomedical and health informatics
Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear a...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

IEEE journal of biomedical and health informatics
Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of ...

TrGPCR: GPCR-Ligand Binding Affinity Prediction Based on Dynamic Deep Transfer Learning.

IEEE journal of biomedical and health informatics
Predicting G protein-coupled receptor (GPCR) -ligand binding affinity plays a crucial role in drug development. However, determining GPCR-ligand binding affinities is time-consuming and resource-intensive. Although many studies used data-driven metho...