摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据N ewsRx记者在韩国釜山的新闻报道，研究表明，"长非编码RNA(lncRNAs)在多种生物学过程中发挥着重要作用，并有助于各种人类疾病的进展和发展"。我们的新闻记者从浦京国立大学的研究中获得了一句话：“因此，有必要从生物标志物检测的角度解读新的INCRNA-疾病关联。已经设计了大量的计算模型来使用机器学习来识别INCRNA-疾病关联。然而，这些模型中许多未能有效地整合异质的生物数据集，这可能导致模型的准确性和性能下降。在本研究中，本文提出了一种新的基于lncRNA表达谱的矩阵因子分解方法，该方法利用lncRNA表达谱识别lncRNA疾病关联(EMFLDA)。矩阵因子分解是一种机器学习方法，不仅在推荐系统中表现出了优异的性能，而且在各种科学领域也表现出了优异的性能。我们还将lncRNA表达谱作为模型的权重，允许异构信息的整合，从而提高了
Abstract
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.”
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