2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)最新文献_第7页

ProteinShop and POSE: bringing robotics and intelligent systems into the field of molecular modeling ProteinShop和POSE:将机器人和智能系统引入分子建模领域

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.116

Ting-Cheng Lu, S. Crivelli, N. Max

引用次数: 0

Minimal marker sets to discriminate among seedlines 最小的标记集来区分种系

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.92

T. Hudson, A. Stapleton, Amy M. Curley

引用次数: 0

Oscillatory dynamics in the mitogen-activated protein kinase cascade 丝裂原活化蛋白激酶级联的振荡动力学

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.102

K. Chiam, V. Bhargava, G. Rajagopal

引用次数: 1

Protein structure prediction using physical-based global optimization and knowledge-guided fragment packing 基于物理全局优化和知识引导片段打包的蛋白质结构预测

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.115

Jinhui Ding, E. Eskow, N. Max, S. Crivelli

引用次数: 2

Computational identification and characterization of Type III secretion substrates III型分泌底物的计算鉴定和表征

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.41

E. Sakk, D. Schneider, S. Cartinhour, C. Myers, Monica Vencato, A. Collmer

{"title":"Computational identification and characterization of Type III secretion substrates","authors":"E. Sakk, D. Schneider, S. Cartinhour, C. Myers, Monica Vencato, A. Collmer","doi":"10.1109/CSBW.2005.41","DOIUrl":"https://doi.org/10.1109/CSBW.2005.41","url":null,"abstract":"Many bacterial pathogens employ a Type III secretion system (TTSS) to deliver specific proteins (or \"substrates\") into a host cytoplasm in order to interfere with defense responses and alter physiology. In this work, we present a computational formalism for characterizing the compositional properties of the Type III secretion signal. While various rule sets derived from empirical observations have been suggested, developing a consistent and comprehensive description of the TTSS signal is still of interest. This problem differs from typical signal peptide classification and identification problems (e.g. - nuclear, chloroplast, mitochondrial signal peptides) because known TTSS substrates lack the similarity expected from signal sequences involved in a similar function (e.g. -from a multiple alignment profile or signal consensus sequence). Using a training set derived from empirically verified substrate sequences in Pseudomonas syringae, we apply divergence measures derived from information theory in order to classify similar patterns and characterize the Type III signal. The TTSS characterization developed in this work leads to a diffuse targeting signal confined to the first 50 amino acids starting from the N-terminus. Finally, using the P. syringae training set, the method is applied to verify and predict substrate candidates in other organisms possessing a TTSS.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116753946","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: 2

Diagnosis and biomarker identification on SELDI proteomics data by ADTBoost ADTBoost对SELDI蛋白质组学数据的诊断和生物标志物鉴定

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.51

Lu-Yong Wang, A. Chakraborty, D. Comaniciu

{"title":"Diagnosis and biomarker identification on SELDI proteomics data by ADTBoost","authors":"Lu-Yong Wang, A. Chakraborty, D. Comaniciu","doi":"10.1109/CSBW.2005.51","DOIUrl":"https://doi.org/10.1109/CSBW.2005.51","url":null,"abstract":"Clinical proteomics is an emerging field that will have great impact on molecular diagnosis, identification of disease biomarkers, drug discovery and clinical trials in the post-genomic era. Protein profiling in tissues and fluids in disease and pathological control and other proteomics techniques will play an important role in molecular diagnosis with therapeutics and personalized healthcare. We introduced a new robust diagnostic method based on ADTboost algorithm, a novel method in proteomics data analysis to improve classification accuracy. It generates classification rules, which are often smaller and easier to interpret. This method often gives most discriminative features, which can be utilized as biomarkers for diagnostic purpose. Also, it has a nice feature of providing a measure of prediction confidence. We carried out this method in Amyotrophic lateral sclerosis disease data acquired by surface enhanced laser desorption/ionization-time-of-flight mass spectrometry experiments. Our method is shown to have outstanding prediction capacity through the cross-validation, ROC analysis results and comparative study. Our molecular diagnosis method provides an efficient way to distinguish ALS disease from neurological controls. The results are expressed in a simple and straightforward alternating decision tree format or conditional format. We identified most discriminative peaks in proteomic data, which can be utilized as biomarkers for diagnosis. ADTboost is not only useful in on proteomic data classification, it can also integrate other clinical, imaging data from heterogeneous sources for early diagnosis. It will have broad application in molecular diagnosis through proteomics and personalized medicine.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"211 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134530028","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

An information fusion approach to controlling complexity in bioinformatics research 生物信息学研究中控制复杂性的信息融合方法

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.22

B. Olsson, B. Gawrońska, T. Ziemke, S. F. Andler, P. Nilsson, A. Persson

引用次数: 5

A general methodology for integration of microarray data 集成微阵列数据的一般方法

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.8

C. Huttenhower, O. Troyanskaya

引用次数: 0

Sequential classification for microarray and clinical data 微阵列和临床数据的顺序分类

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.123

G. Tusch

引用次数: 5

2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05) Pub Date : 2005-08-08 DOI: 10.1109/CSBW.2005.125

Jiaxiong Pi, Yong Shi, Zhengxin Chen

{"title":"Similarity and cluster analysis algorithms for microarrays using R* trees","authors":"Jiaxiong Pi, Yong Shi, Zhengxin Chen","doi":"10.1109/CSBW.2005.125","DOIUrl":"https://doi.org/10.1109/CSBW.2005.125","url":null,"abstract":"Similarity and cluster analysis are important aspects for analyzing microarray data. Based on our perspective of viewing microarrays as time series data, both similarity analysis and cluster analysis are carried out through indexing on time series data using R*-Trees. We have developed algorithms for similarity and cluster analysis on microarray data, and conducted experimental studies and comparative studies. First, our study shows that principle components analysis (PCA) has superiority over several other methods (such as DFT and PAA) as far as distance conservation is concerned. A similarity analysis tool based on PCA has been developed, which is able to explore less R*-Tree nodes before finding its similar counterparts and returns less false positives than other methods. In addition, we also extend R*-Tree's application to cluster analysis. With the aid of R*-Tree indexing, two clustering algorithms. KMeans-R and Hierarchy-R, are proposed as an improved version of K-Means and hierarchical clustering, respectively. Experiments for similarity search and cluster analysis based on proposed algorithms have been carried out and have shown favorable results. Experiments related to yeast cell cycle dataset are reported in this paper.","PeriodicalId":123531,"journal":{"name":"2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124432983","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