首页|Inner Mongolia Agricultural University Reports Findings in Artificial Intelligen ce (Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermop hilus interactions based on a highly accurate semi-supervised learning method)
Inner Mongolia Agricultural University Reports Findings in Artificial Intelligen ce (Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermop hilus interactions based on a highly accurate semi-supervised learning method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Hohhot, People's Republic of China, by NewsRx journalists, research stated, "Lactobacillus delbru eckii subsp. bulgaricus (L. bulgaricus) and Streptococcus thermophilus (S. therm ophilus) are commonly used starters in milk fermentation. Fermentation experimen ts revealed that L. bulgaricus-S. thermophilus interactions (LbStI) substantiall y impact dairy product quality and production." The news correspondents obtained a quote from the research from Inner Mongolia A gricultural University, "Traditional biological humidity experiments are time-co nsuming and labor-intensive in screening interaction combinations, an artificial intelligence-based method for screening interactive starter combinations is nec essary. However, in the current research on artificial intelligence based intera ction prediction in the field of bioinformatics, most successful models adopt su pervised learning methods, and there is a lack of research on interaction predic tion with only a small number of labeled samples. Hence, this study aimed to dev elop a semi-supervised learning framework for predicting LbStI using genomic dat a from 362 isolates (181 per species). The framework consisted of a two-part mod el: a co-clustering prediction model (based on the Kyoto Encyclopedia of Genes a nd Genomes (KEGG) dataset) and a Laplacian regularized least squares prediction model (based on K-mer analysis and gene composition of all isolates datasets). T o enhance accuracy, we integrated the separate outcomes produced by each compone nt of the two-part model to generate the ultimate LbStI prediction results, whic h were verified through milk fermentation experiments. Validation through milk f ermentation experiments confirmed a high precision rate of 85% (17 /20; validated with 20 randomly selected combinations of expected interacting is olates). Our data suggest that the biosynthetic pathways of cysteine, riboflavin , teichoic acid, and exopolysaccharides, as well as the ATP-binding cassette tra nsport systems, contribute to the mutualistic relationship between these starter bacteria during milk fermentation. However, this finding requires further exper imental verification."
HohhotPeople's Republic of ChinaAsiaArtificial IntelligenceEmerging TechnologiesGram-Positive Asporogenous Rod sGram-Positive BacteriaGram-Positive CocciGram-Positive RodsLactobacilla ceaeLactobacillalesLactobacillusLactobacillus delbrueckiiMachineLearnin gStreptococcaceaeStreptococcusStreptococcus thermophilusSupervised Learn ing
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