Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Support Vector Machines. According to news reporting originating from Shandong, People’ s Republic of China, by NewsRx correspondents, research stated, “Genome-wide ass ociation studies have shown that common genetic variants associated with complex diseases are mostly located in non-coding regions, which may not be causal. In addition, the limited number of validated non-coding functional variants makes i t difficult to develop an effective supervised learning model.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Ludong University, “The refore, improving the accuracy of predicting non-coding causal variants has beco me critical. This study aims to build a transfer learning-based machine learning method for predicting regulatory variants to overcome the problem of limited sa mple size. This paper presents a supervised learning method transfer support vec tor machine (TSVM) for massively parallel reporter assays (MPRA) validated regul atory variants prediction. First, uses a convolutional neural network to extract features with transfer learning. Second, the extracted features are selected by random forest method. Third, the selected features are used to train support ve ctor machine for classification. We performed scale sensitivity experiments on t he MPRA dataset and validated the effectiveness of transfer learning.”
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