Int. J. Knowl. Discov. Bioinform.最新文献_第6页

Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data 结合网络拓扑结构改进了转录组学数据中蛋白质相互作用网络的预测

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-07-01 DOI: 10.4018/jkdb.2010070101

Peter E. Larsen, F. Collart, Yang Dai

引用次数: 5

Wave-SOM: A Novel Wavelet-Based Clustering Algorithm for Analysis of Gene Expression Patterns Wave-SOM:一种新的基于小波的基因表达模式分析聚类算法

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-04-01 DOI: 10.4018/jkdb.2010040104

Andrew E. Blanchard, Christopher Wolter, D. McNabb, Eitan Gross

{"title":"Wave-SOM: A Novel Wavelet-Based Clustering Algorithm for Analysis of Gene Expression Patterns","authors":"Andrew E. Blanchard, Christopher Wolter, D. McNabb, Eitan Gross","doi":"10.4018/jkdb.2010040104","DOIUrl":"https://doi.org/10.4018/jkdb.2010040104","url":null,"abstract":"In this paper, the authors present a wavelet-based algorithm (Wave-SOM) to help visualize and cluster oscillatory time-series data in two-dimensional gene expression micro-arrays. Using various wavelet transformations, raw data are first de-noised by decomposing the time-series into low and high frequency wavelet coefficients. Following thresholding, the coefficients are fed as an input vector into a two-dimensional Self-Organizing-Map clustering algorithm. Transformed data are then clustered by minimizing the Euclidean (L2) distance between their corresponding fluctuation patterns. A multi-resolution analysis by Wave-SOM of expression data from the yeast Saccharomyces cerevisiae, exposed to oxidative stress and glucose-limited growth, identified 29 genes with correlated expression patterns that were mapped into 5 different nodes. The ordered clustering of yeast genes by Wave-SOM illustrates that the same set of genes (encoding ribosomal proteins) can be regulated by two different environmental stresses, oxidative stress and starvation. The algorithm provides heuristic information regarding the similarity of different genes. Using previously studied expression patterns of yeast cell-cycle and functional genes as test data sets, the authorsâ€™ algorithm outperformed five other competing programs.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127638943","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: 9

SPCCTDM, a Catalogue for Analysis of Therapeutic Drug Monitoring Related Contents in the Drug Prescription Information 《药品处方信息中治疗药物监测相关内容分析目录》

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-04-01 DOI: 10.4018/jkdb.2010040101

S. Ulrich, P. Baumann, A. Conca, H. Kuss, V. Stieffenhofer, C. Hiemke

引用次数: 9

Clustering Genes Using Heterogeneous Data Sources 利用异构数据源聚类基因

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-04-01 DOI: 10.4018/jkdb.2010040102

Erliang Zeng, Chengyong Yang, Tao Li, G. Narasimhan

{"title":"Clustering Genes Using Heterogeneous Data Sources","authors":"Erliang Zeng, Chengyong Yang, Tao Li, G. Narasimhan","doi":"10.4018/jkdb.2010040102","DOIUrl":"https://doi.org/10.4018/jkdb.2010040102","url":null,"abstract":"Clustering of gene expression data is a standard exploratory technique used to identify closely related genes. Many other sources of data are also likely to be of great assistance in the analysis of gene expression data. This data provides a mean to begin elucidating the large-scale modular organization of the cell. The authors consider the challenging task of developing exploratory analytical techniques to deal with multiple complete and incomplete information sources. The Multi-Source Clustering (MSC) algorithm developed performs clustering with multiple, but complete, sources of data. To deal with incomplete data sources, the authors adopted the MPCK-means clustering algorithms to perform exploratory analysis on one complete source and other potentially incomplete sources provided in the form of constraints. This paper presents a new clustering algorithm MSC to perform exploratory analysis using two or more diverse but complete data sources, studies the effectiveness of constraints sets and robustness of the constrained clustering algorithm using multiple sources of incomplete biological data, and incorporates such incomplete data into constrained clustering algorithm in form of constraints sets.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123938188","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: 17

Infer Species Phylogenies Using Self-Organizing Maps 利用自组织图推断物种系统发育

Int. J. Knowl. Discov. Bioinform. Pub Date : 2010-04-01 DOI: 10.4018/jkdb.2010040103

Xiaoxu Han

{"title":"Infer Species Phylogenies Using Self-Organizing Maps","authors":"Xiaoxu Han","doi":"10.4018/jkdb.2010040103","DOIUrl":"https://doi.org/10.4018/jkdb.2010040103","url":null,"abstract":"With rapid advances in genomics, phylogenetics has turned to phylogenomics due to the availability of large amounts of sequence and genome data. However, incongruence between species trees and gene trees remains a challenge in molecular phylogenetics for its biological and algorithmic complexities. A state-of-the-art gene concatenation approach was proposed to resolve this problem by inferring the species phylogeny using a random combination of widely distributed orthologous genes screened from genomes. However, such an approach may not be a robust solution to this problem because it ignores the fact that some genes are more informative than others in species inference. This paper presents a self-organizing map (SOM) based phylogeny inference method to overcome its weakness. The authorâ€™s proposed algorithm not only demonstrates its superiority to the original gene concatenation method by using same datasets, but also shows its advantages in generalization. This paper illustrates that data missing may not play a negative role in phylogeny inferring. This study presents a method to cluster multispecies genes, estimate multispecies gene entropy and visualize the species patterns through the self-organizing map mining.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124346859","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: 4

Identification of Distinguishing Motifs 区分基元的识别

Int. J. Knowl. Discov. Bioinform. Pub Date : 2007-07-09 DOI: 10.4018/jkdb.2010070104

Wangsen Feng, Lusheng Wang

引用次数: 3

Scaling Unsupervised Risk Stratification to Massive Clinical Datasets 将无监督风险分层扩展到大规模临床数据集

Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/jkdb.2011010103

Z. Syed, I. Rubinfeld

{"title":"Scaling Unsupervised Risk Stratification to Massive Clinical Datasets","authors":"Z. Syed, I. Rubinfeld","doi":"10.4018/jkdb.2011010103","DOIUrl":"https://doi.org/10.4018/jkdb.2011010103","url":null,"abstract":"While rare clinical events, by definition, occur infrequently in a population, the consequences of these events can be drastic. Unfortunately, developing risk stratification algorithms for these conditions requires large volumes of data to capture enough positive and negative cases. This process is slow, expensive, and burdensome to both patients and caregivers. This paper proposes an unsupervised machine learning approach to address this challenge and risk stratify patients for adverse outcomes without use of a priori knowledge or labeled training data. The key idea of the approach is to identify high-risk patients as anomalies in a population. Cases are identified through a novel algorithm that finds an approximate solution to the k-nearest neighbor problem using locality sensitive hashing (LSH) based on p-stable distributions. The algorithm is optimized to use multiple LSH searches, each with a geometrically increasing radius, to find the k-nearest neighbors of patients in a dynamically changing dataset where patients are being added or removed over time. When evaluated on data from the National Surgical Quality Improvement Program (NSQIP), this approach successfully identifies patients at an elevated risk of mortality and rare morbidities. The LSH-based algorithm provided a substantial improvement over an exact k-nearest neighbor algorithm in runtime, while achieving a similar accuracy.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"34 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":"116526562","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

Improving Resiliency in SDN using Routing Tree Algorithms 利用路由树算法提高SDN的弹性

Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2017010104

Kshira Sagar Sahoo, B. Sahoo, Ratnakar Dash, B. K. Mishra

引用次数: 3

Time-Aware Task Allocation for Cloud Computing Environment 云计算环境下的时间感知任务分配

Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2017010101

Sushanta Meher, S. K. Pande, S. K. Panda

引用次数: 1

Animal Actin Phylogeny and RNA Secondary Structure Study 动物肌动蛋白系统发育及RNA二级结构研究

Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2015010104

B. P. Barik

引用次数: 1