AIMC Topic: Nucleotidyltransferases

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Genetic dissection of complex traits using hierarchical biological knowledge.

PLoS computational biology Sep 17, 2021
Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical bio...
Neural Networks, Computer Machine Learning Phenotype Genome-Wide Association Study Polymorphism, Single Nucleotide Computational Biology Systems Biology Models, Genetic Metabolic Networks and Pathways Copper Mutation Glucose Glycine Knowledge Bases Saccharomyces cerevisiae Gene Ontology Hydroxyurea Chromosome Mapping Multifactorial Inheritance Benomyl Nucleotidyltransferases
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A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma.

Briefings in bioinformatics Nov 22, 2024
Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti...
Humans Algorithms Prognosis Deep Learning Liver Neoplasms Carcinoma, Hepatocellular Immune Checkpoint Inhibitors B7-H1 Antigen Membrane Proteins Signal Transduction Programmed Cell Death 1 Receptor Nucleotidyltransferases
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