Int. J. Knowl. Discov. Bioinform.最新文献_第5页

Search for Protein Sequence Homologues that Display Considerable Domain Length Variations 搜索显示相当大的结构域长度变化的蛋白质序列同源物

Int. J. Knowl. Discov. Bioinform. Pub Date : 2011-04-01 DOI: 10.4018/jkdb.2011040104

Eshita Mutt, A. Mitra, R. Sowdhamini

{"title":"Search for Protein Sequence Homologues that Display Considerable Domain Length Variations","authors":"Eshita Mutt, A. Mitra, R. Sowdhamini","doi":"10.4018/jkdb.2011040104","DOIUrl":"https://doi.org/10.4018/jkdb.2011040104","url":null,"abstract":"Independent folding units which have the capability of carrying out biological functions have been classified as â€œprotein domainsâ€ . These minimal structural units lead not only to considerable sequence changes of protein domains of similar folds and functions, but also gives rise to remarkable length variations under evolutionary pressure. Rapid and heuristic sequence search algorithms are generally sensitive and effective in recognizing protein domains that are distantly related within large sequence databases, but are not well-suited to identify remote homologues of varying lengths. An even more challenging aspect is introduced to distinguish reliable hits from a vast number of putative false positives that could have suboptimal sequence similarities. Here, the authors present a data-mining approach that provides stage-specific filters in sequence searches to reliably accumulate remote homologues, which encourages sampling of length variations albeit with a low false positive rate. Realization of such remote homologues with vivid length variations could contribute to better understanding of functional variety within protein domain superfamilies.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126191671","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: 8

Graph-Based Shape Analysis for MRI Classification 基于图的MRI分类形状分析

Int. J. Knowl. Discov. Bioinform. Pub Date : 2011-04-01 DOI: 10.4018/JKDB.2011040102

L. Holder

引用次数: 14

Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach 利用样本生长高效挖掘频繁封闭判别双聚类:FDCluster方法

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-10-01 DOI: 10.4018/jkdb.2010100104

Miao Wang, Xuequn Shang, Shaohua Zhang, Zhanhuai Li

{"title":"Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach","authors":"Miao Wang, Xuequn Shang, Shaohua Zhang, Zhanhuai Li","doi":"10.4018/jkdb.2010100104","DOIUrl":"https://doi.org/10.4018/jkdb.2010100104","url":null,"abstract":"DNA microarray technology has generated a large number of gene expression data. Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. It finds clusters of genes possessing similar characteristics together with biological conditions creating these similarities. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, the authors propose the FDCluster algorithm in order to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses Apriori property and several novel techniques for pruning to mine biclusters efficiently. To increase the space usage, FDCluster also utilizes several techniques to generate frequent closed bicluster without candidate maintenance in memory. The experimental results show that FDCluster is more effective than traditional methods in either single micorarray dataset or multiple microarray datasets. This paper tests the biological significance using GO to show the proposed method is able to produce biologically relevant biclusters.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128402840","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: 12

Improving Prediction Accuracy via Subspace Modeling in a Statistical Geometry Based Computational Protein Mutagenesis 基于统计几何计算蛋白质诱变的子空间建模提高预测精度

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-10-01 DOI: 10.4018/jkdb.2010100103

M. Masso

引用次数: 6

Revealing the Origin and Nature of Drug Resistance of Dynamic Tumour Systems 揭示动态肿瘤系统耐药的起源和性质

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-10-01 DOI: 10.4018/jkdb.2010100102

R. Santiago-Mozos, I. Khan, M. G. Madden

引用次数: 19

Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain 基于层次密度的人脑白质束聚类

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-10-01 DOI: 10.4018/jkdb.2010100101

Junming Shao, K. Hahn, Qinli Yang, A. Wohlschläger, C. Böhm, Nicholas Myers, C. Plant

{"title":"Hierarchical Density-Based Clustering of White Matter Tracts in the Human Brain","authors":"Junming Shao, K. Hahn, Qinli Yang, A. Wohlschläger, C. Böhm, Nicholas Myers, C. Plant","doi":"10.4018/jkdb.2010100101","DOIUrl":"https://doi.org/10.4018/jkdb.2010100101","url":null,"abstract":"Diffusion tensor magnetic resonance imaging (DTI) provides a promising way of estimating the neural fiber pathways in the human brain non-invasively via white matter tractography. However, it is difficult to analyze the vast number of resulting tracts quantitatively. Automatic tract clustering would be useful for the neuroscience community, as it can contribute to accurate neurosurgical planning, tract-based analysis, or white matter atlas creation. In this paper, the authors propose a new framework for automatic white matter tract clustering using a hierarchical density-based approach. A novel fiber similarity measure based on dynamic time warping allows for an effective and efficient evaluation of fiber similarity. A lower bounding technique is used to further speed up the computation. Then the algorithm OPTICS is applied, to sort the data into a reachability plot, visualizing the clustering structure of the data. Interactive and automatic clustering algorithms are finally introduced to obtain the clusters. Extensive experiments on synthetic data and real data demonstrate the effectiveness and efficiency of our fiber similarity measure and show that the hierarchical density-based clustering method can group these tracts into meaningful bundles on multiple scales as well as eliminating noisy fibers.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"51 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132780981","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: 18

Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins 挖掘蛋白质相互作用组网络测量相互作用可靠性和选择枢纽蛋白

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-07-01 DOI: 10.4018/jkdb.2010070102

Young-Rae Cho, A. Zhang

{"title":"Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins","authors":"Young-Rae Cho, A. Zhang","doi":"10.4018/jkdb.2010070102","DOIUrl":"https://doi.org/10.4018/jkdb.2010070102","url":null,"abstract":"High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126234482","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: 10

Bioinformatics Methods for Studying MicroRNA and ARE-Mediated Regulation of Post-Transcriptional Gene Expression 研究MicroRNA和are介导的转录后基因表达调控的生物信息学方法

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-07-01 DOI: 10.4018/jkdb.2010070106

Richipal Singh Bindra, J. Wang, P. Bagga

引用次数: 11

Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling 频繁布尔表达式的挖掘:在基因表达和调控建模中的应用

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-07-01 DOI: 10.4018/jkdb.2010070105

Mohammed J. Zaki, Naren Ramakrishnan, Lizhuang Zhao

引用次数: 16

Discriminative Subgraph Mining for Protein Classification 判别子图挖掘在蛋白质分类中的应用

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-07-01 DOI: 10.4018/jkdb.2010070103

Ning Jin, Calvin Young, Wei Wang

引用次数: 9