首页|Data on Machine Learning Reported by Kishori M. Konwar and Colleagues (Predictin g metabolic modules in incomplete bacterial genomes with MetaPathPredict)
Data on Machine Learning Reported by Kishori M. Konwar and Colleagues (Predictin g metabolic modules in incomplete bacterial genomes with MetaPathPredict)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Revere, United States, b y NewsRx journalists, research stated, “The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challen ging. Computational pipelines for pathway reconstruction that utilize machine le arning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking.” The news correspondents obtained a quote from the research, “Here, we present Me taPathPredict, a software tool that incorporates machine learning models to pred ict the presence of complete KEGG modules within bacterial genomic datasets. Usi ng gene annotation data and information from the KEGG module database, MetaPathP redict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python modul e, and both options are designed to be run locally or on a compute cluster.”
Revere, United States, North and Central America, Bacterial Genome, Cyborgs, Emerging Technologies, Genomics, Machine Le arning
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