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Computational Biology

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Drug Repositioning via Multi-View Representation Learning With Heterogeneous Graph Neural Network.

IEEE journal of biomedical and health informatics
Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associati...

Knowledge Graph Neural Network With Spatial-Aware Capsule for Drug-Drug Interaction Prediction.

IEEE journal of biomedical and health informatics
Uncovering novel drug-drug interactions (DDIs) plays a pivotal role in advancing drug development and improving clinical treatment. The outstanding effectiveness of graph neural networks (GNNs) has garnered significant interest in the field of DDI pr...

Multimodal Drug Target Binding Affinity Prediction Using Graph Local Substructure.

IEEE journal of biomedical and health informatics
Predicting the binding affinity of drug target is essential to reduce drug development costs and cycles. Recently, several deep learning-based methods have been proposed to utilize the structural or sequential information of drugs and targets to pred...

BINDTI: A Bi-Directional Intention Network for Drug-Target Interaction Identification Based on Attention Mechanisms.

IEEE journal of biomedical and health informatics
The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In vitro experimental methods are expensive, laborious, and time-consuming. Deep learning has witnessed promising progress in DTI prediction. However, how t...

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...

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...

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...