AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Computational Biology

Showing 101 to 110 of 4028 articles

Clear Filters

PLPTP: A Motif-based Interpretable Deep Learning Framework Based on Protein Language Models for Peptide Toxicity Prediction.

Journal of molecular biology
Peptide toxicity prediction holds significant importance in drug development and biotechnology, as accurately identifying toxic peptide sequences is crucial for designing safer peptide-based drugs. This study proposes a deep learning-based model for ...

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.

BMC bioinformatics
The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural ...

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data.

Nature communications
Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular...

SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Computers in biology and medicine
Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth and tumor formation. Understanding the intricate interactions between somatic mutations and drugs was crucial for advancing our knowledge of the underl...

Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.

Scientific reports
This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed g...

Integrating machine learning and multi-omics analysis to reveal nucleotide metabolism-related immune genes and their functional validation in ischemic stroke.

Frontiers in immunology
BACKGROUND: Ischemic stroke (IS) is a major global cause of death and disability, linked to nucleotide metabolism imbalances. This study aimed to identify nucleotide metabolism-related genes associated with IS and explore their roles in disease mecha...

Identification of CACNB1 protein as an actionable therapeutic target for hepatocellular carcinoma via metabolic dysfunction analysis in liver diseases: An integrated bioinformatics and machine learning approach for precise therapy.

International journal of biological macromolecules
In addition to histological evaluation for nonalcoholic fatty liver disease (NAFLD), a comprehensive analysis of the metabolic landscape is urgently needed to categorize patients into distinct subgroups for precise treatment. In this study, a total o...

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.

Journal of bioinformatics and computational biology
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Curr...

CasPro-ESM2: Accurate identification of Cas proteins integrating pre-trained protein language model and multi-scale convolutional neural network.

International journal of biological macromolecules
Cas proteins (CRISPR-associated protein) are the core components of the CRISPR-Cas system, playing critical roles in defending against foreign DNA and RNA invasions. Identifying Cas proteins can provide deeper insights into the immune mechanisms of t...