Dataset Information

Identification of discriminative characteristics for clusters from biologic data with InforBIO software.

ABSTRACT: BACKGROUND: There are a number of different methods for generation of trees and algorithms for phylogenetic analysis in the study of bacterial taxonomy. Genotypic information, such as SSU rRNA gene sequences, now plays a more prominent role in microbial systematics than does phenotypic information. However, the integration of genotypic and phenotypic information for polyphasic studies is necessary for the classification and identification of microbes. Thus, we devised an algorithm that objectively identifies discriminative characteristics for focused clusters on generated trees from a dataset composed of coded data, such as phenotypic information. Moreover, this algorithm has been integrated into the polyphasic analysis software, InforBIO. RESULTS: We developed a differential-character-finding algorithm based on information measures and used this algorithm to identify the characteristic that best discriminates operational taxonomic unit clusters. For all characteristics in a dataset, the algorithm estimates commonality in focused clusters and diversity among clusters by scoring based on Shannon's and relative entropies. All the characteristics selected for scoring are equally weighted. Thresholds for the scores are defined to identify discriminative characteristics for clusters efficiently from a database. The unique feature of the algorithm, which is implemented in the InforBIO software, is that it can identify the phenotypic characteristics that discriminate and are associated with the clusters of a phylogenetic tree. We successfully applied this algorithm to the study of phylogenetic clusters of Pseudomonas species. CONCLUSION: The algorithm in the InforBIO software is a novel and useful approach for microbial polyphasic studies. The algorithm can also be applied to diverse cluster analyses. The InforBIO software is available from the download site http://wdcm.nig.ac.jp/inforbio/. This software is free for personal but not commercial use.

SUBMITTER: Tanaka N

PROVIDER: S-EPMC1973088 | biostudies-other | 2007

REPOSITORIES: biostudies-other

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Identification of discriminative characteristics for clusters from biologic data with InforBIO software.

Tanaka Naoto N Uchino Masataka M Miyazaki Satoru S Sugawara Hideaki H

BMC bioinformatics 20070802

<h4>Background</h4>There are a number of different methods for generation of trees and algorithms for phylogenetic analysis in the study of bacterial taxonomy. Genotypic information, such as SSU rRNA gene sequences, now plays a more prominent role in microbial systematics than does phenotypic information. However, the integration of genotypic and phenotypic information for polyphasic studies is necessary for the classification and identification of microbes. Thus, we devised an algorithm that ob ...[more]

PMID: 17683520

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Project description:BackgroundNovel diabetes phenotypes were proposed by the Europeans through cluster analysis, but Chinese community diabetes populations might exhibit different characteristics. This study aims to explore the clinical characteristics of novel diabetes subgroups under data-driven analysis in Chinese community diabetes populations.MethodsWe used K-means cluster analysis in 6369 newly diagnosed diabetic patients from eight centers of the REACTION (Risk Evaluation of cAncers in Chinese diabeTic Individuals) study. The cluster analysis was performed based on age, body mass index, glycosylated hemoglobin, homeostatic modeled insulin resistance index, and homeostatic modeled pancreatic β-cell functionality index. The clinical features were evaluated with the analysis of variance (ANOVA) and chi-square test. Logistic regression analysis was done to compare chronic kidney disease and cardiovascular disease risks between subgroups.ResultsOverall, 2063 (32.39%), 658 (10.33%), 1769 (27.78%), and 1879 (29.50%) populations were assigned to severe obesity-related and insulin-resistant diabetes (SOIRD), severe insulin-deficient diabetes (SIDD), mild age-associated diabetes mellitus (MARD), and mild insulin-deficient diabetes (MIDD) subgroups, respectively. Individuals in the MIDD subgroup had a low risk burden equivalent to prediabetes, but with reduced insulin secretion. Individuals in the SOIRD subgroup were obese, had insulin resistance, and a high prevalence of fatty liver, tumors, family history of diabetes, and tumors. Individuals in the SIDD subgroup had severe insulin deficiency, the poorest glycemic control, and the highest prevalence of dyslipidemia and diabetic nephropathy. Individuals in MARD subgroup were the oldest, had moderate metabolic dysregulation and the highest risk of cardiovascular disease.ConclusionThe data-driven approach to differentiating the status of new-onset diabetes in the Chinese community was feasible. Patients in different clusters presented different characteristics and risks of complications.

Dataset Information

Identification of discriminative characteristics for clusters from biologic data with InforBIO software.

Publications

Identification of discriminative characteristics for clusters from biologic data with InforBIO software.

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