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Gillies, C.E., Otto, E.A. and Vega, W.V. (2016) tarSVM: Improving the Accuracy of Variant Calls Derived from Microfluidic PCR-Based Targeted Next Generation Sequencing Using a Support Vector Machine. BMC Bioinformatics, 17, 233.
https://doi.org/10.1186/s12859-016-1108-4

has been cited by the following article:

• TITLE: Medical Data Visualization Analysis and Processing Based on Machine Learning

  AUTHORS: Tong Wang, Lei Zhao, Yanfeng Cao, Zhijian Qu, Panjing Li

  KEYWORDS: Data Visualization Analysis, Machine Learning, KNN, SVM, RF

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.11, November 28, 2018

  ABSTRACT: Trying to provide a medical data visualization analysis tool, the machine learning methods are introduced to classify the malignant neoplasm of lung within the medical database MIMIC-III (Medical Information Mart for Intensive Care III, USA). The K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) are selected as the predictive tool. Based on the experimental result, the machine learning predictive tools are integrated into the medical data visualization analysis platform. The platform software can provide a flexible medical data visualization analysis tool for the doctors. The related practice indicates that visualization analysis result can be generated based on simple steps for the doctors to do some research work on the data accumulated in hospital, even they have not taken special data analysis training.