首页|Data on Machine Learning Published by a Researcher at Pukyong National Universit y (LncRNA Expression Profile-Based Matrix Factorization for Predicting lncRNA- D isease Association)
Data on Machine Learning Published by a Researcher at Pukyong National Universit y (LncRNA Expression Profile-Based Matrix Factorization for Predicting lncRNA- D isease Association)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Busan, South Korea, by N ewsRx journalists, research stated, “Long non-coding RNAs (lncRNAs) play signifi cant roles in multiple biological processes and contribute to the progression an d development of various human diseases.” Our news correspondents obtained a quote from the research from Pukyong National University: “Therefore, it is necessary to decipher novel lncRNA-disease associ ations from the perspective of biomarker detection. Numerous computational model s have been designed to identify lncRNA-disease associations using machine learn ing. However, many of these models fail to effectively incorporate heterogeneous biological datasets, which can lead to reduced model accuracy and performance. In this study, we propose a novel lncRNA expression profile-based matrix factori zation method that applies lncRNA expression profiles to identify lncRNA-disease association (EMFLDA). Matrix factorization is a machine learning method that ex hibits excellent performance not only in recommender systems, but also in variou s scientific areas. We also applied lncRNA expression profiles as weights for th e proposed model, which allowed for the integration of heterogeneous information and thereby improved performance.”
Pukyong National UniversityBusanSout h KoreaAsiaCyborgsEmerging TechnologiesMachine Learning
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