2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献_第8页

Handling Data Sparseness in Gene Network Reconstruction 基因网络重构中的数据稀疏性处理

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594900

G. B. Bezerra, T.V. Barra, F. V. Zuben, L. Castro

引用次数: 1

Fuzzy Profile Hidden Markov Models for Protein Sequence Analysis 蛋白质序列分析的模糊隐马尔可夫模型

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594950

Niranjan P. Bidargaddi, M. Chetty, J. Kamruzzaman

{"title":"Fuzzy Profile Hidden Markov Models for Protein Sequence Analysis","authors":"Niranjan P. Bidargaddi, M. Chetty, J. Kamruzzaman","doi":"10.1109/CIBCB.2005.1594950","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594950","url":null,"abstract":"Profile HMMs based on classical hidden Markov models have been widely applied for alignment and classification of protein sequence families. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile hidden Markov model to overcome the limitations of the statistical independence assumption of probability theory. The strong correlations and the sequence preference involved in the protein structures make fuzzy architecture based models as suitable candidates for building profiles of a given family since fuzzy set can handle uncertainties better than classical methods. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures using Choquet integrals which is extended to fuzzy Baum-Welch parameter estimation algorithm for profiles. It was built and tested on widely studied globin and kinase family sequences and its performance was compared with classical HMM. A comparative analysis based on Log-Likelihood (LL) scores of sequences and Receiver Operating Characteristic (ROC) demonstrates the superiority of fuzzy profile HMMs over the classical profile model.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114993635","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}

引用次数: 3

A New Semi-Supervised Subspace Clustering Algorithm on Fitting Mixture Models 混合模型拟合中一种新的半监督子空间聚类算法

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594919

Young Bun Kim, Jean X. Gao

引用次数: 2

Toward Protein Structure Analysis with Self-Organizing Maps 基于自组织图谱的蛋白质结构分析

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 1900-01-01 DOI: 10.1109/CIBCB.2005.1594961

L. Hamel, Gongqin Sun, Jing Zhang

{"title":"Toward Protein Structure Analysis with Self-Organizing Maps","authors":"L. Hamel, Gongqin Sun, Jing Zhang","doi":"10.1109/CIBCB.2005.1594961","DOIUrl":"https://doi.org/10.1109/CIBCB.2005.1594961","url":null,"abstract":"Establishing structure-function relationships on the proteomic scale is a unique challenge faced by bioinformatics and molecular biosciences. Large protein families represent natural libraries of analogues of a given catalytic or protein function, thus making them ideal targets for the investigation of structure-function relationships in proteins. To this end, we have developed a new technique for analyzing large amounts of detailed molecular structure information focusing on the functional centers of homologous proteins. Our approach uses unsupervised machine learning, in particular, self-organizing maps. The information captured by a self-organizing map and stored in its reference models highlights the essential structure of the proteins under investigation and can be effectively used to study detailed structural differences and similarities among homologous proteins. Our preliminary results obtained with a prototype based on these techniques demonstrate that we can classify proteins and identify common and unique structures within a family and, more importantly, identify common and unique structural features of different conformations of the same protein. The approach developed here outperforms many of today’s structure analysis tools. These tools are usually either limited by the number of proteins they can process at the same time or they are limited by the structural resolution they can accommodate, that is, many of the structural analysis tools that can handle multiple proteins at the same time limit themselves to secondary structure analysis and therefore miss fine structural nuances within proteins.","PeriodicalId":330810,"journal":{"name":"2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121902714","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}

引用次数: 5