AIMC Topic: Computational Biology

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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...
Computational Biology Antimicrobial Peptides Peptides Antineoplastic Agents Neural Networks, Computer Machine Learning Humans
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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...
Humans Antineoplastic Agents Deep Learning Cell Line, Tumor Neural Networks, Computer Neoplasms Computational Biology
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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...
Drug Repositioning Neural Networks, Computer Deep Learning Humans 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...
Ligands Databases, Protein Receptors, G-Protein-Coupled Humans Protein Binding Deep Learning Computational Biology
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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...
Intrinsically Disordered Proteins Neural Networks, Computer ROC Curve Software Computational Biology Convolutional Neural Networks Databases, Protein
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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...
Computational Biology Machine Learning Neoplasms Peptides Humans
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Breast cancer prediction based on gene expression data using interpretable machine learning techniques.

Scientific reports Mar 4, 2025
Breast cancer remains a global health burden, with an increase in deaths related to this particular cancer. Accurately predicting and diagnosing breast cancer is important for treatment development and survival of patients. This study aimed to accura...
Humans Female Breast Neoplasms Machine Learning Gene Expression Regulation, Neoplastic Support Vector Machine Gene Expression Profiling Computational Biology
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Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach.

Scientific reports Mar 4, 2025
The discovery of unique microRNA (miR) patterns and their corresponding genes in sarcoma patients indicates their involvement in cancer development and suggests their potential use in medical management. MiRs were identified from The Cancer Genome At...
Sarcoma Kaplan-Meier Estimate Gene Expression Regulation, Neoplastic Machine Learning Databases, Genetic Humans Middle Aged Prognosis MicroRNAs Computational Biology Biomarkers, Tumor Female Male
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Identification of ALDH2 as a novel target for the treatment of acute kidney injury in kidney transplantation based on WGCNA and machine learning algorithms and exploration of its potential mechanism of action using animal experiments.

Frontiers in immunology Mar 4, 2025
BACKGROUND: Acute kidney injury (AKI) after kidney transplantation is one of the main causes of graft loss and poor patient prognosis, and it is important to explore new targets for treating AKI in kidney transplantation.
Disease Models, Animal Acute Kidney Injury Gene Expression Profiling Algorithms Aldehyde Dehydrogenase, Mitochondrial Male Gene Regulatory Networks Animals Kidney Transplantation Computational Biology Humans Rats Machine Learning
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