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

Nodal distance algorithm: calculating a phylogenetic tree comparison metric 节点距离算法:计算一个系统发育树比较度量

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188933

John Bluis, Dong-Guk Shin

引用次数: 41

An open multiple instance learning framework and its application in drug activity prediction problems 开放式多实例学习框架及其在药物活性预测中的应用

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188929

Xin Huang, Shu‐Ching Chen, M. Shyu

{"title":"An open multiple instance learning framework and its application in drug activity prediction problems","authors":"Xin Huang, Shu‐Ching Chen, M. Shyu","doi":"10.1109/BIBE.2003.1188929","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188929","url":null,"abstract":"In this paper, a powerful open Multiple Instance Learning (MIL) framework is proposed. Such an open framework is powerful since different sub-methods can be plugged into the framework to generate different specific Multiple Instance Learning algorithms. In our proposed framework, the Multiple Instance Learning problem is first converted to an unconstrained optimization problem by the Minimum Square Error (MSE) criterion, and then the framework can be constructed with an open form of hypothesis and gradient search method. The proposed Multiple Instance Learning framework is applied to the drug activity problems in bioinformatics applications. Specifically, experiments are conducted on the Musk-I dataset to predict the binding activity of drug molecules. In the experiments, an algorithm with the exponential hypothesis model and the Quasi-Newton method is embedded into our proposed framework. We compare our proposed framework with other existing algorithms and the experimental results show that our proposed framework yields a good accuracy of classification, which demonstrates the feasibility and effectiveness of our framework.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"51 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":"121361398","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

Vessel extraction techniques and algorithms: a survey 血管提取技术与算法综述

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188957

C. Kirbas, Francis K. H. Quek

引用次数: 164

A robotic device for minimally invasive breast interventions with real-time MRI guidance 一种实时MRI引导下的微创乳房介入机器人装置

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188946

B. Larson, N. Tsekos, A. Erdman

引用次数: 21

Enhanced biclustering on expression data 增强表达数据的双聚类

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188969

Jiong Yang, Haixun Wang, Wei Wang, Philip S. Yu

{"title":"Enhanced biclustering on expression data","authors":"Jiong Yang, Haixun Wang, Wei Wang, Philip S. Yu","doi":"10.1109/BIBE.2003.1188969","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188969","url":null,"abstract":"Microarrays are one of the latest breakthroughs in experimental molecular biology, which provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and are already producing huge amount of valuable data. The concept of bicluster was introduced by Cheng and Church (2000) to capture the coherence of a subset of genes and a subset of conditions. A set of heuristic algorithms were also designed to either find one bicluster or a set of biclusters, which consist of iterations of masking null values and discovered biclusters, coarse and fine node deletion, node addition, and the inclusion of inverted data. These heuristics inevitably suffer from some serious drawback. The masking of null values and discovered biclusters with random numbers may result in the phenomenon of random interference which in turn impacts the discovery of high quality biclusters. To address this issue and to further accelerate the biclustering process, we generalize the model of bicluster to incorporate null values and propose a probabilistic algorithm (FLOC) that can discover a set of k possibly overlapping biclusters simultaneously. Furthermore, this algorithm can easily be extended to support additional features that suit different requirements at virtually little cost. Experimental study on the yeast gene expression data shows that the FLOC algorithm can offer substantial improvements over the previously proposed algorithm.","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":"121084402","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: 342