AIMC Topic: Metabolic Networks and Pathways
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A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels.
BioMed research international
Jun 16, 2020
OBJECTIVE: Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy.
SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice.
Plant science : an international journal of experimental plant biology
May 23, 2020
Gene or pathway functional similarities are important information for researchers. However, these similarities are often described sparsely and qualitatively. The latent semantic analysis of Arabidopsis thaliana (Arabidopsis) Gene Ontology (GO) data ...
Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.
Metabolomics : Official journal of the Metabolomic Society
Mar 25, 2020
INTRODUCTION: To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructi...
The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors.
Journal of chemical information and modeling
Feb 24, 2020
Metabolism of drugs affects their absorption, distribution, efficacy, excretion, and toxicity profiles. Metabolism is routinely assessed experimentally using recombinant enzymes, human liver microsome, and animal models. Unfortunately, these experime...
Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression.
Scientific reports
Feb 17, 2020
Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalati...
Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.
Cell systems
Jan 8, 2020
Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. H...
Reinforcement Learning for Bioretrosynthesis.
ACS synthetic biology
Dec 30, 2019
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are requi...
Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.
Computers in biology and medicine
Oct 9, 2019
BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.
Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction.
Trends in biotechnology
Aug 28, 2019
Metabolomics is a powerful tool to rationally guide the metabolic engineering of synthetic bioproduction pathways. Current reports indicate great potential to further develop metabolomics-directed synthetic bioproduction. Advanced mass metabolomics m...
Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.
Molecular genetics and metabolism
Aug 19, 2019
Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, w...
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