Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.最新文献_第2页

Detecting experimental noises in protein-protein interactions with iterative sampling and model-based clustering 基于迭代采样和模型聚类的蛋白质相互作用实验噪声检测

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188977

Hiroshi Mamitsuka

{"title":"Detecting experimental noises in protein-protein interactions with iterative sampling and model-based clustering","authors":"Hiroshi Mamitsuka","doi":"10.1109/BIBE.2003.1188977","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188977","url":null,"abstract":"One of the most important issues in current molecular biology is to build exact networks of protein-protein interactions. Recently developed high-throughput experimental techniques accumulate a vast amount of protein-protein interaction data, but it is well known that data reliability has not reached at a satisfactory level. In this paper we attempt to computationally detect experimental errors or noises presumably contained in the protein-protein interaction data by an iterative sampling method using the learning of a stochastic model as its subroutine. The method repeats two steps of selecting examples that can be regarded as non-noises, and training the component algorithm with the selected examples alternately. Noise candidates are selected as the examples having the smallest average likelihoods computed by previously obtained stochastic models. We empirically evaluated the method with other two methods by using both synthetic and real data sets. We examined the effect of noises and data sizes by using medium- and large-sized synthetic data sets that contain noises added intentionally. The results obtained by the medium-sized synthetic data sets show that the significance level of the performance difference between the method and the two other methods has more pronounced for higher noise ratios. Further experiments show that this experimental finding was also true of a large-scale data set. The performance advantage of the method was further confirmed by the experiments using a real protein-protein interaction data set.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198396","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: 1

A data mining method to predict transcriptional regulatory sites based on differentially expressed genes in human genome 基于人类基因组差异表达基因预测转录调控位点的数据挖掘方法

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188966

Hsien-Da Huang, Huei-Lin Chang, T. Tsou, Baw-Jhiune Liu, Jorng-Tzong Horng

引用次数: 5

Influence of the thermal treatment applied to PAN gel on its length change and generated force 热处理对聚丙烯腈凝胶长度变化及生成力的影响

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188964

H. Tamagawa, F. Nogata, Toyotaka Watanabe, A. Abe, S. Popovic

引用次数: 0

Analyzing the Escherichia coli gene expression data by a multilayer adjusted tree organizing map 利用多层调整树组织图分析大肠杆菌基因表达数据

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188965

Ning Wei, L. Gruenwald, T. Conway

{"title":"Analyzing the Escherichia coli gene expression data by a multilayer adjusted tree organizing map","authors":"Ning Wei, L. Gruenwald, T. Conway","doi":"10.1109/BIBE.2003.1188965","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188965","url":null,"abstract":"Using the DNA microarray technology, biologists have thousands of array data available. Discovering the function relations between genes and their involvements in biological processes depends on the ability to efficiently process and quantitatively analyze large amounts of array data. Clustering algorithms are among the popular tools that can be used to help biologists achieve their goals. Although some existing research projects employed clustering algorithms on biological data, none of them has examined the Escherichia coli (E. coli) gene expression data. This paper proposes a clustering algorithm called Multilayer Adjusted Tree Organizing Map (MA TOM) to analyze the E. coli gene expression data. In a semi-supervised manner, MATOM constructs a multilayer map, and at the same time, removes noise data in the previously trained maps in order to improve the training process. This paper then presents the clustering results produced by MATOM and other existing clustering algorithms using the E. coli gene expression data, and a new evaluation method to assess them. The results show that MATOM performs the best in terms of percentage of genes that are clustered correctly.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122716356","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: 0

Prediction of contact maps using support vector machines 使用支持向量机预测接触图

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188926

Ying Zhao, G. Karypis

引用次数: 57

Evolving bubbles for prostate surface detection from TRUS images 从TRUS图像中检测前列腺表面的演化气泡

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188936

Fan Shao, K. Ling, W. Ng

引用次数: 3

Time series analysis of gene expression and location data 基因表达和位置数据的时间序列分析

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188967

Chen-Hsiang Yeang, T. Jaakkola

引用次数: 25

Determination of the minimum sample size in microarray experiments to cluster genes using k-means clustering 利用k-均值聚类技术进行基因聚类的微阵列实验中最小样本量的确定

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188979

Fang-Xiang Wu, W. Zhang, A. Kusalik

{"title":"Determination of the minimum sample size in microarray experiments to cluster genes using k-means clustering","authors":"Fang-Xiang Wu, W. Zhang, A. Kusalik","doi":"10.1109/BIBE.2003.1188979","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188979","url":null,"abstract":"Gene expression profiles obtained from time-series microarray experiments can reveal important information about biological processes. However, conducting such experiments is costly and time consuming. The cost and time required are linearly proportional to sample size. Therefore, it is worthwhile to provide a way to determine the minimal number of samples or trials required in a microarray experiment. One of the uses of microarray hybridization experiments is to group together genes with similar patterns of the expression using clustering techniques. In this paper, the k-means clustering technique is used. The basic idea of our approach is an incremental process in which testing, analysis and evaluation are integrated and iterated. The process is terminated when the evaluation of the results of two consecutive experiments shows they are sufficiently close. Two measures of \"closeness\" are proposed and two real microarray datasets are used to validate our approach. The results show that the sample size required to cluster genes in these two datasets can be reduced; i.e. the same results can be achieved with less cost. The approach can be used with other clustering techniques as well.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123035823","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: 11

Vessel extraction in medical images by 3D wave propagation and traceback 基于三维波传播与回溯的医学图像血管提取

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188944

C. Kirbas, Francis K. H. Quek

引用次数: 22

An assessment of a metric space database index to support sequence homology 一个度量空间数据库索引支持序列同源性的评估

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188976

Rui Mao, Weijia Xu, Neha Singh, Daniel P. Miranker

{"title":"An assessment of a metric space database index to support sequence homology","authors":"Rui Mao, Weijia Xu, Neha Singh, Daniel P. Miranker","doi":"10.1109/BIBE.2003.1188976","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188976","url":null,"abstract":"Hierarchical metric-space clustering methods have been commonly used to organize proteomes into taxonomies. Consequently, it is often anticipated that hierarchical clustering can be leveraged as a basis for scalable database index structures capable of managing the hyper-exponential growth of sequence data. M-tree is one such data structure specialized for the management of large data sets on disk. We explore the application of M-trees to the storage and retrieval of peptide sequence data. Exploiting a technique first suggested by Myers (1994), we organize the database as records of fixed length substrings. Empirical results are promising. However, metric-space indexes are subject to \"the curse of dimensionality\" and the ultimate performance of an index is sensitive to the quality of the initial construction of the index. We introduce new hierarchical bulk-load algorithm that alternates between top-down and bottom-up clustering to initialize the index. Using the Yeast Proteomes, the bi-directional bulk load produces a more effective index than the existing M-tree initialization algorithms.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128517346","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: 30