Chang Gung University

Chang Gung Memorial Hospital, Linkou

Chun-Hsien Chen

Hsin-Yao Wang

Chih-Kuang Chen

Jang-Jih Lu

Yi-Ju Tseng

Wan-Ying Lin

Tsui-Ping Liu

A method for establishing a computer-aided data interpretation model for immune diseases by immunomarkers and visualization is revealed. First combine a plurality of immunomarkers into an immunomarker panel. Then collect test data of a plurality of subjects measured by the immunomarker panel, and disease diagnosis information of the subjects for establishment of an immunomarker-panel testing database. Next new subjects are tested by the immunomarker panel. The data obtained and the corresponding information in the immunomarker-panel testing database are processed by unsupervised machine learning algorithm to get a computer-aided data interpretation model showing comparison of case distribution patterns. The method provides real-time analysis of multiple data to medical professionals for their reference. Thereby the correctness, the timeliness and the reproducibility of the interpretation result for the diagnosis and treatment of immune diseases are all improved.

Method for establishing computer-aided data interpretation model for immune diseases by immunomarkers and visualization

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.

Method of creating characteristic peak profiles of mass spectra and identification model for analyzing and identifying microorganizm

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Taoyuan, Taiwan

Chun-Hsien Chen