首页|University of Chinese Academy of Sciences Reports Findings in Machine Learning (A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning)
University of Chinese Academy of Sciences Reports Findings in Machine Learning (A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning)
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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 the subject of a report. According to news report- ing originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of pro- teins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connec- tions between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored.” Funders for this research include Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China. The news reporters obtained a quote from the research from the University of Chinese Academy of Sciences, “In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (F ) is identified as the most crucial feature. While, in the further three-class LLPS- Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83.”
BeijingPeople’s Republic of ChinaAsiaAmyloidCyborgsEmerging TechnologiesMachine LearningPeptidesPeptides and ProteinsProteins
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Wiley
