Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.最新文献_第3页

Uses of multiagents systems for simulation of MAPK pathway 使用多智能体系统模拟MAPK通路

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188982

G. Querrec, V. Rodin, J. Abgrall, S. Kerdélo, J. Tisseau

引用次数: 16

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

An empirical comparison of tools for phylogenetic footprinting 系统发育足迹分析工具的经验比较

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188931

M. Blanchette, Samson Kwong, M. Tompa

引用次数: 9

An algorithm to reconstruct a target DNA sequence from its spectrum connected at a given level 一种从在给定水平上连接的谱中重建目标DNA序列的算法

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188947

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

{"title":"An algorithm to reconstruct a target DNA sequence from its spectrum connected at a given level","authors":"Fang-Xiang Wu, W. Zhang, A. Kusalik","doi":"10.1109/BIBE.2003.1188947","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188947","url":null,"abstract":"In order to sequence a target DNA, it is first cleaved into many shorter overlapping fragments by chemical or physical techniques. The nucleotide sequence of each fragment is then determined (read) by established methods. The set of all read fragments which cover the target DNA sequence is called its spectrum. It is believed that the shortest superstring of a spectrum is the best candidate for the target DNA sequence. The general problem of finding the shortest superstring for any given set of strings s is NP-hard. Fortunately, the biological instance of this problem is easier. It is not likely that two read fragments, each consisting of several hundred letters, which come from consecutive locations on the target DNA sequence have an overlap of only a few letters; typically, the overlap will be longer. Thus one may reasonably assume that two strings in the spectrum have significant overlap (connectivity) if they come from consecutive locations on the target DNA sequence. A class of important instances satisfying this assumption are those whose spectra are from DNA microarrays. This assumption allows us to claim and show the following: if the spectrum S of a target DNA sequence is substring-free and connected at level t, and the target DNA sequence has no repeats of size t or larger, then there exists an algorithm to reconstruct the target DNA sequence in the linear time O(|S|) after an overlap graph of the spectrum is built.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"44 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":"134073678","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

DHC: a density-based hierarchical clustering method for time series gene expression data DHC:一种基于密度的时间序列基因表达数据的分层聚类方法

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188978

D. Jiang, J. Pei, A. Zhang

{"title":"DHC: a density-based hierarchical clustering method for time series gene expression data","authors":"D. Jiang, J. Pei, A. Zhang","doi":"10.1109/BIBE.2003.1188978","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188978","url":null,"abstract":"Clustering the time series gene expression data is an important task in bioinformatics research and biomedical applications. Recently, some clustering methods have been adapted or proposed. However, some concerns still remain, such as the robustness of the mining methods, as well as the quality and the interpretability of the mining results. In this paper, we tackle the problem of effectively clustering time series gene expression data by proposing algorithm DHC, a density-based, hierarchical clustering method. We use a density-based approach to identify the clusters such that the clustering results are of high quality and robustness. Moreover, the mining result is in the form of a density tree, which uncovers the embedded clusters in a data set. The inner-structures, the borders and the outliers of the clusters can be further investigated using the attraction tree, which is an intermediate result of the mining. By these two trees, the internal structure of the data set can be visualized effectively. Our empirical evaluation using some real-world data sets show that the method is effective, robust and scalable. It matches the ground truth provided by bioinformatics experts very well in the sample data sets.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"126 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":"114581068","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: 193

A computational pipeline for protein structure prediction and analysis at genome scale 基因组尺度下蛋白质结构预测与分析的计算管道

Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. Pub Date : 2003-03-10 DOI: 10.1109/BIBE.2003.1188923

M. Shah, S. Passovets, Dongsup Kim, K. Ellrott, Li Wang, Inna Vokler, P. LoCascio, Dong Xu, Ying Xu

{"title":"A computational pipeline for protein structure prediction and analysis at genome scale","authors":"M. Shah, S. Passovets, Dongsup Kim, K. Ellrott, Li Wang, Inna Vokler, P. LoCascio, Dong Xu, Ying Xu","doi":"10.1109/BIBE.2003.1188923","DOIUrl":"https://doi.org/10.1109/BIBE.2003.1188923","url":null,"abstract":"Traditionally, protein 3D structures are solved using experimental techniques, like X-ray crystallography or nuclear magnetic resonance (NMR). While these experimental techniques have been the main workhorse for protein structure studies in the past few decades, it is becoming increasingly apparent that they alone cannot keep up with the production rate of protein sequences. Fortunately, computational techniques for protein structure predictions have matured to such a level that they can complement the existing experimental techniques. In this paper, we present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is a threading-based protein structure prediction system, called PROSPECT, which we have been developing for the past few years. The pipeline consists of seven logical phases, utilizing a dozen tools. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. A number of genome-scale applications have been carried out on microbial genomes. Here we present one genome-scale application on Caenorhabditis elegans.","PeriodicalId":178814,"journal":{"name":"Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings.","volume":"98 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":"121572553","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: 26