2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops最新文献_第2页

Ontology algebra for composition of protein data sources 蛋白质数据源组成的本体代数

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425412

A. Sidhu, T. Dillon, E. Chang

引用次数: 2

ailSemantic Web-based data representation and reasoning applied to disease mechanism and pharmacology 基于语义的数据表示和推理在疾病机制和药理学中的应用

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425411

Xiaoyan A. Qu, R. C. Gudivada, A. Jegga, Eric K. Neumann, Bruce J. Aronow

引用次数: 9

V-Lab-Protein: Virtual Collaborative Lab for protein sequence analysis V-Lab-Protein:蛋白质序列分析的虚拟协作实验室

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425417

Jong Youl Choi, Youngik Yang, Sun Kim, Dennis Gannon

引用次数: 8

Predicting protein-protein interactions based on BP neural network 基于BP神经网络的蛋白质相互作用预测

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425393

Zhiqiang Ma, Chunguang Zhou, Linying Lu, Yanan Ma, Pingping Sun, Ying Cui

引用次数: 15

Incorporating constraints from low resolution density map in ab initio structure prediction using Rosetta 结合低分辨率密度图约束的Rosetta从头算结构预测

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425402

Y. Lu, C. Strauss, Jing He

{"title":"Incorporating constraints from low resolution density map in ab initio structure prediction using Rosetta","authors":"Y. Lu, C. Strauss, Jing He","doi":"10.1109/BIBMW.2007.4425402","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425402","url":null,"abstract":"We have developed a new method for adding constraints derived from low resolution density maps to Rosetta ab initio prediction method. This method incorporates the geometrical constraints of the helix skeleton that can be detected from a low resolution density map. We propose a 2-stage approach to predict the backbone of a protein from a low resolution map. In stage one, a small set of possible topologies will be predicted for the helix skeleton [1]. This paper describes the second stage that is to predict the backbone of the protein from a low resolution density map. A constraint scoring function was developed and incorporated in the Rosetta simulation process. The entire density map is only used for the final selection among the possible backbones that satisfy the constraints. Our method was tested with 16 mainly alpha-helical proteins ranging from 50 to 150 residues. 12 of the 16 proteins show improved accuracy for both the top 1 prediction and the best of the top 5 predictions. The average improvement of the RMSD to native is 4.76 A for the top 1 model and 3.05 A for the best of the top 5 ranked models when the density map is applied.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133153289","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

INDARE - An indexed DAG of regular expressions for selecting position frequency matrices 用于选择位置频率矩阵的正则表达式的索引DAG

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425418

Meeyoung Park, Jubin Sanghvi, D. Dinakarpandian

引用次数: 0

Structural characterization of RNA-binding sites of proteins: Preliminary results 蛋白质rna结合位点的结构表征:初步结果

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425401

F. Towfic, Cornelia Caragea, D. Dobbs, D. Gemperline, Feihong Wu, Vasant G Honavar

{"title":"Structural characterization of RNA-binding sites of proteins: Preliminary results","authors":"F. Towfic, Cornelia Caragea, D. Dobbs, D. Gemperline, Feihong Wu, Vasant G Honavar","doi":"10.1109/BIBMW.2007.4425401","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425401","url":null,"abstract":"We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA...etc), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the protein data bank. Our analysis of surface roughness, solid angle and CX value of amino acid residues for each of the protein chains in the dataset shows that: The protein-RNA interface residues tend to be protruding compared to non-interface residues and tend to have higher surface roughness and exhibit moderately convex or concave solid angles. Furthermore, the protein chains in protein-RNA interfaces that contain Viral RNA and rRNA significantly differ from those that contain dsRNA, mRNA siRNA, snRNA, SRP RNA and tRNA with respect to their CX values. The results of this analysis sug gests the possibility of using such structural features to reliably identify protein-RNA interface residues when the structure of the protein is available but the structures of complexes formed by the protein with RNA are not.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116980605","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

Enhancing the computation of approximate solutions of the protein structure determination problem through global constraints for discrete crystal lattices 利用离散晶格的全局约束增强了蛋白质结构确定问题近似解的计算能力

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425398

A. D. Palù, Enrico Pontelli, A. Dovier

引用次数: 2

Comparative analysis of binary logistic regression to artificial neural networks in predicting precursor sequence cleavage 二元逻辑回归与人工神经网络预测前体序列解理的比较分析

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425407

A. Tegge, S. Rodriguez-Zas, J. Sweedler, B. Southey

{"title":"Comparative analysis of binary logistic regression to artificial neural networks in predicting precursor sequence cleavage","authors":"A. Tegge, S. Rodriguez-Zas, J. Sweedler, B. Southey","doi":"10.1109/BIBMW.2007.4425407","DOIUrl":"https://doi.org/10.1109/BIBMW.2007.4425407","url":null,"abstract":"Bioinformatic predictions of neuropeptides resulting from enzymatic cleavages of precursors enable a range of follow-up studies that are aided by accurate predictions. A comparative study of the performance of complementary cleavage prediction models has been undertaken. Binary logistic and artificial neural network (ANN) models were created using various strategies and trained and tested on bovine and rat precursors with experimental cleavage information. Multiple criteria were used to compare 4 logistic regression models with varying properties and 8 ANN with varying structures. All models had high specificity (>90%) and sensitivity ranged from 68% to 100%. ANN based on well-represented amino acid locations performed similarly or slightly worse than networks based on all amino acid locations. Logistic parameter estimates aided in the identification of amino acids associated with cleavage. No model was superior across data sets and thus, prediction of neuropeptides should rely on multiple model specifications and comprehensive training data sets.","PeriodicalId":260286,"journal":{"name":"2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130639740","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

A Bayesian approach for the analysis of recurrent events with chronic granulomatous disease 慢性肉芽肿病复发事件的贝叶斯分析方法

2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops Pub Date : 2007-11-01 DOI: 10.1109/BIBMW.2007.4425414

Chi-Chang Chang

引用次数: 0