Pub Date : 2009-11-01DOI: 10.1109/BIBMW.2009.5332104
Li An, Zoran Obradovic, Desmond Smith, Olivier Bodenreider, Vasileios Megalooikonomou
Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression maps on mouse brain. The experimental results show that the detected association rules make sense biologically. By inspecting the obtained clusters and the genes having the gene functions of frequent itemsets, interesting clues were discovered that provide valuable insight to biological scientists. Moreover, discovered association rules can be potentially used to predict gene functions based on similarity of gene expression maps.
{"title":"Mining Association Rules among Gene Functions in Clusters of Similar Gene Expression Maps.","authors":"Li An, Zoran Obradovic, Desmond Smith, Olivier Bodenreider, Vasileios Megalooikonomou","doi":"10.1109/BIBMW.2009.5332104","DOIUrl":"10.1109/BIBMW.2009.5332104","url":null,"abstract":"<p><p>Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression maps on mouse brain. The experimental results show that the detected association rules make sense biologically. By inspecting the obtained clusters and the genes having the gene functions of frequent itemsets, interesting clues were discovered that provide valuable insight to biological scientists. Moreover, discovered association rules can be potentially used to predict gene functions based on similarity of gene expression maps.</p>","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2009 ","pages":"254-259"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4307020/pdf/nihms654700.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33345106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Statistical machine learning is a field that combines algorithmic ideas with foundational concepts from probability and statistics. This combination makes statistical machine learning an essential tool for computational biology, in part because probabilistic notions are inherent in biology (arising, e.g., via thermodynamics, recombination and germline mutation) and in part because of the incomplete nature of most biological data sets. I will present several examples of applications of statistical machine learning to problems in biology, in the areas of protein functional annotation, protein structural modeling, protein structure prediction and multipopulation linkage and association analysis.
{"title":"Statistical Machine Learning and Computational Biology","authors":"Michael I. Jordan","doi":"10.1109/BIBM.2007.68","DOIUrl":"https://doi.org/10.1109/BIBM.2007.68","url":null,"abstract":"Statistical machine learning is a field that combines algorithmic ideas with foundational concepts from probability and statistics. This combination makes statistical machine learning an essential tool for computational biology, in part because probabilistic notions are inherent in biology (arising, e.g., via thermodynamics, recombination and germline mutation) and in part because of the incomplete nature of most biological data sets. I will present several examples of applications of statistical machine learning to problems in biology, in the areas of protein functional annotation, protein structural modeling, protein structure prediction and multipopulation linkage and association analysis.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"78 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78217735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/BIBM.2014.6999358
Jiamin Yuan, Li Huang, Fuping Xu, H. Guan, Zhimin Yang
[Objective]: To analyze dialectical thinking that three different prescriptions treating insomnia which Professor Zhimin Yang used. And to provide a reference for the research of Chinese medicine experts in dialectical thinking. [Methods]: First, we collected three empirical prescriptions that Professor Zhi-Min Yang used from her medical cases about insomnia. We also investigated the patients' demographic feature, historical feature, four diagnostic techniques of Traditional Chinese Medicine (TCM) feature, Pittsburgh Sleep Quality Index(PSQI), anxiety scale(SAS), depression scale(SDS). Second, we observed the whole treatment process, and defined the standard of curative effects by PSQI reduced rate between before-treatment and post-treatment. Third, to ensure that results have clinical utility, we selected the valid cases to analyze Filter preliminary features from three empirical prescriptions by chi-square test. And then, to analyze the relations among the preliminary indications by decision tree. Finally, to sum up three different prescriptions' dialectical thinking. [Results]: We had collected 78 valid cases of three prescriptions. We investigated 206 features of each case, and filtered 71 preliminary features from three empirical prescriptions by chi-square test. Decision-tree picked up 6 factors, 7 regulations. The accuracy rate of identification was 91.03%.[Conclusion]: The decision tree model can accurately and expeditiously to extract the characteristics from empirical prescriptions, and provide a reference for the research of Chinese medicine experts in dialectical thinking.
{"title":"Analysis of dialectical thinking about different treatments for the same disease based on decision tree model","authors":"Jiamin Yuan, Li Huang, Fuping Xu, H. Guan, Zhimin Yang","doi":"10.1109/BIBM.2014.6999358","DOIUrl":"https://doi.org/10.1109/BIBM.2014.6999358","url":null,"abstract":"[Objective]: To analyze dialectical thinking that three different prescriptions treating insomnia which Professor Zhimin Yang used. And to provide a reference for the research of Chinese medicine experts in dialectical thinking. [Methods]: First, we collected three empirical prescriptions that Professor Zhi-Min Yang used from her medical cases about insomnia. We also investigated the patients' demographic feature, historical feature, four diagnostic techniques of Traditional Chinese Medicine (TCM) feature, Pittsburgh Sleep Quality Index(PSQI), anxiety scale(SAS), depression scale(SDS). Second, we observed the whole treatment process, and defined the standard of curative effects by PSQI reduced rate between before-treatment and post-treatment. Third, to ensure that results have clinical utility, we selected the valid cases to analyze Filter preliminary features from three empirical prescriptions by chi-square test. And then, to analyze the relations among the preliminary indications by decision tree. Finally, to sum up three different prescriptions' dialectical thinking. [Results]: We had collected 78 valid cases of three prescriptions. We investigated 206 features of each case, and filtered 71 preliminary features from three empirical prescriptions by chi-square test. Decision-tree picked up 6 factors, 7 regulations. The accuracy rate of identification was 91.03%.[Conclusion]: The decision tree model can accurately and expeditiously to extract the characteristics from empirical prescriptions, and provide a reference for the research of Chinese medicine experts in dialectical thinking.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"14 1","pages":"197-200"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87077492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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