AIMC Topic: Computational Biology
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DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks.
BMC bioinformatics
Jan 13, 2025
Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature informat...
GRL-PUL: predicting microbe-drug association based on graph representation learning and positive unlabeled learning.
Molecular omics
Jan 13, 2025
Extensive research has confirmed the widespread presence of microorganisms in the human body and their crucial impact on human health, with drugs being an effective method of regulation. Hence it is essential to identify potential microbe-drug associ...
Analysis and validation of serum biomarkers in brucellosis patients through proteomics and bioinformatics.
Frontiers in cellular and infection microbiology
Jan 13, 2025
INTRODUCTION: This study aims to utilize proteomics, bioinformatics, and machine learning algorithms to identify diagnostic biomarkers in the serum of patients with acute and chronic brucellosis.
UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.
BMC bioinformatics
Jan 11, 2025
Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose ...
Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique.
Computers in biology and medicine
Jan 10, 2025
Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and p...
AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus.
Methods (San Diego, Calif.)
Jan 10, 2025
Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Syntheti...
deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.
Journal of chemical information and modeling
Jan 10, 2025
Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has beco...
A novel machine learning-based immune prognostic signature for improving clinical outcomes and guiding therapy in colorectal cancer: an integrated bioinformatics and experimental study.
BMC cancer
Jan 10, 2025
Immune cells are pivotal components in the tumor microenvironment (TME), which can interact with tumor cells and significantly influence cancer progression and therapeutic outcomes. Therefore, classifying cancer patients based on the status of immune...
SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma.
Scientific reports
Jan 10, 2025
Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying molecular mechanisms and prognosis prediction presenting ongoing challenges. In this study, we integrated data from multiple public datasets, including...
A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.
PLoS computational biology
Jan 10, 2025
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from ot...
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