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公开(公告)号：US20180173847A1
公开(公告)日：2018-06-21
申请号：US15382212
申请日：2016-12-16
申请人： JANG-JIH LU , CHUN-HSIEN CHEN , HSIN-YAO WANG , YING-HAO WEN
发明人： JANG-JIH LU , CHUN-HSIEN CHEN , HSIN-YAO WANG , YING-HAO WEN
摘要： A method of establishing a machine learning model for cancer anticipation includes collecting test results of a plurality of tumor markers of a plurality of eligible individuals and corresponding conditions of cancer; performing a variable selection process on the collected data to select a plurality of robust variables; and using the selected variables, numerals, and conditions of cancer by cooperating with a machine learning method to establish a cancer anticipation model. A method of detecting cancer by using a plurality of tumor markers in a machine learning model for cancer anticipation is also provided.
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公开(公告)号：US20190012430A1
公开(公告)日：2019-01-10
申请号：US15644886
申请日：2017-07-10
申请人： JANG-JIH LU , CHUN-HSIEN CHEN , HSIN-YAO WANG , TSUI-PING LIU
发明人： JANG-JIH LU , CHUN-HSIEN CHEN , HSIN-YAO WANG , TSUI-PING LIU
摘要： A method of creating characteristic peak profiles of mass spectra and identification model for analyzing and identifying microorganisms are provided. MALDI-TOF MS data of microorganisms having the same feature are gathered. Discretization of the data is performed. Density-based clustering is used to find m/z values of spectral peaks with high probability of occurrence from the discretized data. A characteristic MS peak profile is created for every specific feature of microorganisms. Every such a characteristic profile forms a feature template. The mass spectrum of each known isolate is matched against all the feature templates and a number of matched vectors are obtained. The matched vectors are then concatenated into a single “integrated vector.” Then, a machine learning method and the integrated vectors generated from all known isolates are used to create a classification model for microorganism identification.
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