Abstract: A method of creating characteristic profiles of mass spectra and identification model for analyzing and identifying microorganisms includes collecting m/z data of microorganisms having same features from MALDI-TOF MS; classifying the microorganisms; classifying the collected set of m/z data as a plurality of subsets; creating modified subsets by applying KDE to the subsets; creating first characteristic MS profiles based on the modified subsets; summarizing into a second characteristic MS profile; repeating above six steps to create second characteristic MS profiles; creating a training set comprising first matched vectors; training a machine learning system using the training set to establish a feature classification model; using MALDI-TOF MS to analyze microorganisms having unknown features; comparing m/z of MALDI-TOF MS spectrum of the microorganisms having unknown features with second characteristic MS profiles to obtain second matched vectors; using the feature classification model analyzing the second m

Abstract: A method of creating characteristic profiles of mass spectra and identification model for analyzing and identifying microorganisms includes obtaining data of MALDI-TOF MS of microorganisms having same features; using a kernel density estimation to generate characteristic profiles of an m/z of the data; creating a characteristic MS profile based on the m/z; repeating above three step until characteristic MS profiles of features of the microorganisms is obtained; comparing m/z of MALDITOF MS spectrum of known microorganisms with the characteristic profiles to obtain first matched vectors; using a machine learning method to establish a feature classification model; using MALDI-TOF MS to analyze microorganisms having unknown features; comparing the m/z of MALDI-TOF MS spectrum of the microorganisms having unknown features with the characteristic MS profiles to obtain second matched vectors; using the feature classification model to analyze the second matched vectors; and identifying the microorganisms having the

Abstract: Repeat sequences are the most abundant in the extragenic region of genomes, while a large number of regulatory elements are found in this region. The invention attempts to mine rules on how combinations of individual binding sites are distributed in repeat sequences. These mined association rules would facilitate identifying gene classes regulated by similar mechanisms and accurately predicting regulatory elements. Herein, the combinations of transcription factor binding sites in the repeat sequences are obtained, and data mining techniques are applied to mine the association rules from the combinations of binding sites. In addition, the associations are further pruned to remove insignificant associations and obtain a set of discovered associations. The discovered association rules are used to partially classify the repeat sequences in the repeat sequence database.