AIMC Topic: Computational Biology

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

IEEE journal of biomedical and health informatics Mar 6, 2025
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...
Drug Repositioning Semantics Databases, Factual Computational Biology Machine Learning Algorithms Humans Neural Networks, Computer
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Dual Representation Learning for Predicting Drug-Side Effect Frequency Using Protein Target Information.

IEEE journal of biomedical and health informatics Mar 6, 2025
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...
Humans Deep Learning Drug-Related Side Effects and Adverse Reactions Computational Biology Proteins
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Prediction of Drug-Target Interactions With High- Quality Negative Samples and a Network-Based Deep Learning Framework.

IEEE journal of biomedical and health informatics Mar 6, 2025
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...
Deep Learning Neural Networks, Computer Humans Pharmaceutical Preparations Drug Discovery Computational Biology Proteins Gene Ontology
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MDTL-ACP: Anticancer Peptides Prediction Based on Multi-Domain Transfer Learning.

IEEE journal of biomedical and health informatics Mar 6, 2025
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...
Antineoplastic Agents Computational Biology Antimicrobial Peptides Machine Learning Peptides Humans Neural Networks, Computer
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Decoding Drug Response With Structurized Gridding Map-Based Cell Representation.

IEEE journal of biomedical and health informatics Mar 6, 2025
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...
Antineoplastic Agents Deep Learning Neural Networks, Computer Neoplasms Cell Line, Tumor Computational Biology Humans
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Enhancing Drug Repositioning Through Local Interactive Learning With Bilinear Attention Networks.

IEEE journal of biomedical and health informatics Mar 6, 2025
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...
Humans Neural Networks, Computer Drug Repositioning Deep Learning Computational Biology
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TrGPCR: GPCR-Ligand Binding Affinity Prediction Based on Dynamic Deep Transfer Learning.

IEEE journal of biomedical and health informatics Mar 6, 2025
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...
Deep Learning Protein Binding Receptors, G-Protein-Coupled Computational Biology Ligands Databases, Protein Humans
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TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery.

IEEE journal of biomedical and health informatics Mar 6, 2025
Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since...
Humans Machine Learning Antineoplastic Agents Computational Biology Synthetic Lethal Mutations Neoplasms Drug Discovery
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PredIDR2: Improving accuracy of protein intrinsic disorder prediction by updating deep convolutional neural network and supplementing DisProt data.

International journal of biological macromolecules Mar 5, 2025
Intrinsically disordered proteins (IDPs) or regions (IDRs) are widespread in proteomes, and involved in several important biological processes and implicated in many diseases. Many computational methods for IDR prediction are being developed to decre...
Computational Biology Intrinsically Disordered Proteins Neural Networks, Computer Databases, Protein Convolutional Neural Networks Software ROC Curve
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StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy.

Computers in biology and medicine Mar 5, 2025
The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to pre...
Peptides Computational Biology Machine Learning Neoplasms Humans
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