首页|Data on Machine Learning Reported by HuiPing Liao and Colleagues (Analyzing domain features of small proteins using a machine-learning method)
Data on Machine Learning Reported by HuiPing Liao and Colleagues (Analyzing domain features of small proteins using a machine-learning method)
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NETL
NSTL
Wiley
New research on Machine Learning is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary.” Financial supporters for this research include National Key Research and Development Program of China, Natural Science Foundation of Shandong Province. Our news journalists obtained a quote from the research, “In this study, the InterPro tool and the maximum correlation method were utilized to analyze functional domains of SPs. The purpose was to identify important functional domains that can indicate the essential differences between small and large protein sequences. First, the small and large proteins were represented by their functional domains via a one-hot scheme. Then, the MaxRel method was adopted to evaluate the relationships between each domain and the target variable, indicating small or large protein. The top 36 domain features were selected for further investigation. Among them, 14 were deemed to be highly related to SPs because they were annotated to SPs more frequently than large proteins. We found the involvement of functional domains, such as ubiquitin-conjugating enzyme/RWD-like, nuclear transport factor 2 domain, and alpha subunit of guanine nucleotide-binding protein (G-protein) in regulating the biological function of SPs. The involvement of these domains has been confirmed by other recent studies.”
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningPeptidesPeptides and ProteinsProteins
NETL
NSTL
Wiley
