AIMC Topic: Proto-Oncogene Proteins c-met

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A prognostic biomarker of disulfidptosis constructed by machine learning framework model as potential reporters of pancreatic adenocarcinoma.

Cellular signalling Aug 28, 2024
BACKGROUND: Pancreatic adenocarcinoma (PAAD), known for its high lethality, has not been thoroughly explored in terms of its mechanisms and patterns of immune infiltration. Disulfidptosis, a newly identified mode of cell death, is likely associated w...
Cell Line, Tumor Gene Expression Regulation, Neoplastic Biomarkers, Tumor Pancreatic Neoplasms Proto-Oncogene Proteins c-met Apoptosis Adenocarcinoma Humans Prognosis Machine Learning
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Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data.

Molecular biology of the cell Feb 16, 2022
Internalin B-mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. ...
Artificial Intelligence Ligands Single Molecule Imaging Machine Learning Proto-Oncogene Proteins c-met
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Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening.

Briefings in bioinformatics Sep 20, 2022
Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among N...
Proto-Oncogene Proteins c-met B-Cell Activation Factor Receptor Receptors, Artificial Histamine H2 Antagonists Neurokinin-1 Receptor Antagonists Cytokine Receptor gp130 Calcitonin Receptor-Like Protein Ligands Receptor, Metabotropic Glutamate 5 Receptors, Cytoplasmic and Nuclear Receptors, Aryl Hydrocarbon Deep Learning Receptors, Muscarinic Receptors, Calcitriol Receptors, Gastrointestinal Hormone Receptor-Like Protein Tyrosine Phosphatases, Class 2 Receptors, Polymeric Immunoglobulin
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DL-CNV: A deep learning method for identifying copy number variations based on next generation target sequencing.

Mathematical biosciences and engineering : MBE Sep 30, 2019
Copy number variations (CNVs) play an important role in many types of cancer. With the rapid development of next generation sequencing (NGS) techniques, many methods for detecting CNVs of a single sample have emerged: (i) require genome-wide data of ...
ROC Curve Genome, Human In Situ Hybridization, Fluorescence Lung Neoplasms Exons Proto-Oncogene Proteins c-met Receptor, ErbB-2 Genome-Wide Association Study High-Throughput Nucleotide Sequencing False Positive Reactions Reproducibility of Results DNA Copy Number Variations Carcinoma, Non-Small-Cell Lung Algorithms Humans Databases, Factual Area Under Curve Sensitivity and Specificity Deep Learning
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