Computational systems bioinformatics. Computational Systems Bioinformatics Conference最新文献_第4页

A HAUSDORFF-BASED NOE ASSIGNMENT ALGORITHM USING PROTEIN BACKBONE DETERMINED FROM RESIDUAL DIPOLAR COUPLINGS AND ROTAMER PATTERNS. 一种基于hausdorff的基于偶极偶联和旋转体模式的蛋白质骨架分配算法。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0015

Jianyang Zeng, C. Tripathy, Pei Zhou, B. Donald

{"title":"A HAUSDORFF-BASED NOE ASSIGNMENT ALGORITHM USING PROTEIN BACKBONE DETERMINED FROM RESIDUAL DIPOLAR COUPLINGS AND ROTAMER PATTERNS.","authors":"Jianyang Zeng, C. Tripathy, Pei Zhou, B. Donald","doi":"10.1142/9781848162648_0015","DOIUrl":"https://doi.org/10.1142/9781848162648_0015","url":null,"abstract":"High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. One of the main bottlenecks in NMR structure determination is the interpretation of NMR data to obtain a sufficient number of accurate distance restraints by assigning nuclear Overhauser effect (NOE) spectral peaks to pairs of protons. The difficulty in automated NOE assignment mainly lies in the ambiguities arising both from the resonance degeneracy of chemical shifts and from the uncertainty due to experimental errors in NOE peak positions. In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment (HANA), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue37, 39, employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each rotamer from a statistically diverse library, and drives the selection of optimal position-specific rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn(3) +tn log t), where t is the maximum number of rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol η) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90% assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 Å and all-heavy-atom RMSD < 2.5 Å from reference structures that were determined either by X-ray crystallography or traditional NMR approaches. These results show that our NOE assignment algorithm can be successfully applied to protein NMR spectra to obtain high-quality structures.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"2008 1","pages":"169-181"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64003466","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

Knowledge representation and data mining for biological imaging. 生物成像的知识表示与数据挖掘。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0027

W. Ahmed

{"title":"Knowledge representation and data mining for biological imaging.","authors":"W. Ahmed","doi":"10.1142/9781848162648_0027","DOIUrl":"https://doi.org/10.1142/9781848162648_0027","url":null,"abstract":"Biological and pharmaceutical research relies heavily on microscopically imaging cell populations for understanding their structure and function. Much work has been done on automated analysis of biological images, but image analysis tools are generally focused only on extracting quantitative information for validating a particular hypothesis. Images contain much more information than is normally required for testing individual hypotheses. The lack of symbolic knowledge representation schemes for representing semantic image information and the absence of knowledge mining tools are the biggest obstacles in utilizing the full information content of these images. In this paper we first present a graph-based scheme for integrated representation of semantic biological knowledge contained in cellular images acquired in spatial, spectral, and temporal dimensions. We then present a spatio-temporal knowledge mining framework for extracting non-trivial and previously unknown association rules from image data sets. This mechanism can change the role of biological imaging from a tool used to validate hypotheses to one used for automatically generating new hypotheses. Results for an apoptosis screen are also presented.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"311-4"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64003634","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

On the accurate construction of consensus genetic maps. 论共识遗传图谱的准确构建。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Yonghui Wu, Timothy J Close, Stefano Lonardi

引用次数: 0

Voting algorithms for the motif finding problem. 基序查找问题的投票算法。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Xiaowen Liu, Bin Ma, Lusheng Wang

{"title":"Voting algorithms for the motif finding problem.","authors":"Xiaowen Liu, Bin Ma, Lusheng Wang","doi":"","DOIUrl":"","url":null,"abstract":"Unlabelled: Finding motifs in many sequences is an important problem in computational biology, especially in identification of regulatory motifs in DNA sequences. Let c be a motif sequence. Given a set of sequences, each is planted with a mutated version of c at an unknown position, the motif finding problem is to find these planted motifs and the original c. In this paper, we study the VM model of the planted motif problem, which is proposed by Pevzner and Sze. We give a simple Selecting One Voting algorithm and a more powerful Selecting k Voting algorithm. When the length of motif and the number of input sequences are large enough, we prove that the two algorithms can find the unknown motif consensus with high probability. In the proof, we show why a large number of input sequences is so important for finding motifs, which is believed by most researchers. Experimental results on simulated data also support the claim. Selecting k Voting algorithm is powerful, but computational intensive. To speed up the algorithm, we propose a progressive filtering algorithm, which improves the running time significantly and has good accuracy in finding motifs. Our experimental results show that Selecting k Voting algorithm with progressive filtering performs very well in practice and it outperforms some best known algorithms.Availability: The software is available upon request.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 ","pages":"37-47"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28336171","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 probabilistic coding based quantum genetic algorithm for multiple sequence alignment. 基于概率编码的多序列比对量子遗传算法。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0002

Hongwei Huo, Qiao-Luan Xie, Xubang Shen, V. Stojkovic

{"title":"A probabilistic coding based quantum genetic algorithm for multiple sequence alignment.","authors":"Hongwei Huo, Qiao-Luan Xie, Xubang Shen, V. Stojkovic","doi":"10.1142/9781848162648_0002","DOIUrl":"https://doi.org/10.1142/9781848162648_0002","url":null,"abstract":"This paper presents an original Quantum Genetic algorithm for Multiple sequence ALIGNment (QGMALIGN) that combines a genetic algorithm and a quantum algorithm. A quantum probabilistic coding is designed for representing the multiple sequence alignment. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The features of implicit parallelism and state superposition in quantum mechanics and the global search capability of the genetic algorithm are exploited to get efficient computation. A set of well known test cases from BAliBASE2.0 is used as reference to evaluate the efficiency of the QGMALIGN optimization. The QGMALIGN results have been compared with the most popular methods (CLUSTALX, SAGA, DIALIGN, SB_PIMA, and QGMALIGN) results. The QGMALIGN results show that QGMALIGN performs well on the presenting biological data. The addition of genetic operators to the quantum algorithm lowers the cost of overall running time.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 1","pages":"15-26"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64000317","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

Detecting pathways transcriptionally correlated with clinical parameters. 检测途径转录与临床参数相关。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0022

I. Ulitsky, R. Shamir

引用次数: 13

GaborLocal: peak detection in mass spectrum by Gabor filters and Gaussian local maxima. Gabor滤波器和高斯局部最大值在质谱中的峰检测。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Nha Nguyen, Heng Huang, Soontorn Oraintara, An Vo

{"title":"GaborLocal: peak detection in mass spectrum by Gabor filters and Gaussian local maxima.","authors":"Nha Nguyen, Heng Huang, Soontorn Oraintara, An Vo","doi":"","DOIUrl":"","url":null,"abstract":"Mass Spectrometry (MS) is increasingly being used to discover disease related proteomic patterns. The peak detection step is one of most important steps in the typical analysis of MS data. Recently, many new algorithms have been proposed to increase true position rate with low false position rate in peak detection. Most of them follow two approaches: one is denoising approach and the other one is decomposing approach. In the previous studies, the decomposition of MS data method shows more potential than the first one. In this paper, we propose a new method named GaborLocal which can detect more true peaks with a very low false position rate. The Gaussian local maxima is employed for peak detection, because it is robust to noise in signals. Moreover, the maximum rank of peaks is defined at the first time to identify peaks instead of using the signal-to-noise ratio and the Gabor filter is used to decompose the raw MS signal. We perform the proposed method on the real SELDI-TOF spectrum with known polypeptide positions. The experimental results demonstrate our method outperforms other common used methods in the receiver operating characteristic (ROC) curve.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 ","pages":"85-96"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28336175","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

Consistent alignment of metabolic pathways without abstraction. 一致的排列代谢途径没有抽象。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01

Ferhat Ay, Tamer Kahveci, Valerie de Crécy-Lagard

{"title":"Consistent alignment of metabolic pathways without abstraction.","authors":"Ferhat Ay, Tamer Kahveci, Valerie de Crécy-Lagard","doi":"","DOIUrl":"","url":null,"abstract":"Pathways show how different biochemical entities interact with each other to perform vital functions for the survival of organisms. Similarities between pathways indicate functional similarities that are difficult to identify by comparing the individual entities that make up those pathways. When interacting entities are of single type, the problem of identifying similarities reduces to graph isomorphism problem. However, for pathways with varying types of entities, such as metabolic pathways, alignment problem is more challenging. Existing methods, often, address the metabolic pathway alignment problem by ignoring all the entities except for one type. This kind of abstraction reduces the relevance of the alignment significantly as it causes losses in the information content. In this paper, we develop a method to solve the pairwise alignment problem for metabolic pathways. One distinguishing feature of our method is that it aligns reactions, compounds and enzymes without abstraction of pathways. We pursue the intuition that both pairwise similarities of entities (homology) and their organization (topology) are crucial for metabolic pathway alignment. In our algorithm, we account for both by creating an eigenvalue problem for each entity type. We enforce the consistency by considering the reachability sets of the aligned entities. Our experiments show that, our method finds biologically and statistically significant alignments in the order of seconds for pathways with approximately 100 entities.","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. Computational Systems Bioinformatics Conference","volume":"7 ","pages":"237-48"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28337726","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

Using relative importance methods to model high-throughput gene perturbation screens. 使用相对重要性方法模拟高通量基因扰动筛选。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0020

Ying Jin, Naren Ramakrishnan, L. Heath, R. Helm

引用次数: 5

Fast multisegment alignments for temporal expression profiles. 快速多段比对时间表达谱。

Computational systems bioinformatics. Computational Systems Bioinformatics Conference Pub Date : 2008-01-01 DOI: 10.1142/9781848162648_0028

A. Smith, M. Craven

引用次数: 8