Predicting Lactobacillus delbrueckii subsp.bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method  

作　　者：Shujuan Yang Mei Bai Weichi Liu Weicheng Li Zhi Zhong Lai-Yu Kwok Gaifang Dong Zhihong Sun 

机构地区：[1]Key Laboratory of Dairy Biotechnology and Engineering,Ministry of Education,Inner Mongolia Agricultural University,Hohhot 010018,China [2]Key Laboratory of Dairy Products Processing,Ministry of Agriculture and Rural Affairs,Inner Mongolia Agricultural University,Hohhot 010018,China [3]Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China [4]Collaborative Innovative Center for Lactic Acid Bacteria and Fermented Dairy Products,Ministry of Education,Inner Mongolia Agricultural University,Hohhot 010018,China [5]College of Computer and Information Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China [6]Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry,Hohhot 010018,China

出　　处：《Science China(Life Sciences)》2025年第2期558-574,共17页中国科学(生命科学英文版)

基　　金：supported by the National Key Research and Development Program of China(2022YFD2100700);the National Natural Science Foundation of China(32325040);Basic Scientific Research Business Fee Project of Universities Directly(BR22-14-01);the National Dairy Science and Technology Innovation Center(2022-Open Subject-6);Inner Mongolia Natural Science Foundation Project(2021MS06023);Inner Mongolia Science&Technology planning project(2022YFSJ0017);the earmarked fund for China Agricultural Research System(CARS36)。

摘　　要：Lactobacillus delbrueckii subsp.bulgaricus(L.bulgaricus)and Streptococcus thermophilus(S.thermophilus)are commonly used starters in milk fermentation.Fermentation experiments revealed that L.bulgaricus-S.thermophilus interactions(Lb St I)substantially impact dairy product quality and production.Traditional biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations,an artificial intelligence-based method for screening interactive starter combinations is necessary.However,in the current research on artificial intelligence based interaction prediction in the field of bioinformatics,most successful models adopt supervised learning methods,and there is a lack of research on interaction prediction with only a small number of labeled samples.Hence,this study aimed to develop a semi-supervised learning framework for predicting Lb St I using genomic data from 362 isolates(181per species).The framework consisted of a two-part model:a co-clustering prediction model(based on the Kyoto Encyclopedia of Genes and Genomes(KEGG)dataset)and a Laplacian regularized least squares prediction model(based on K-mer analysis and gene composition of all isolates datasets).To enhance accuracy,we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate Lb St I prediction results,which were verified through milk fermentation experiments.Validation through milk fermentation experiments confirmed a high precision rate of 85%(17/20;validated with 20 randomly selected combinations of expected interacting isolates).Our data suggest that the biosynthetic pathways of cysteine,riboflavin,teichoic acid,and exopolysaccharides,as well as the ATP-binding cassette transport systems,contribute to the mutualistic relationship between these starter bacteria during milk fermentation.However,this finding requires further experimental verification.The presented model and data are valuable resources for academics and industry professionals interested in screening

关 键 词：Lactobacillus delbrueckii subsp.bulgaricus and Streptococcus thermophilus interaction prediction semi-supervised learning dairy starter artificial intelligence milk fermentation 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]