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

IPC分类号： G06F19/24 ,  G06N7/00

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.

公开(公告)号：US10930371B2

公开(公告)日：2021-02-23

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

IPC分类号： G16B40/00 ,  G06N7/00 ,  G16B40/10 ,  G06N20/00

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.