2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)最新文献_第10页

Comparison of tandem mass spectrometry search methods to identify neuropeptides 串联质谱法鉴定神经肽的比较研究

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112530

Malik N. Akhtar, B. Southey, K. Porter, J. Sweedler, S. Rodriguez-Zas

{"title":"Comparison of tandem mass spectrometry search methods to identify neuropeptides","authors":"Malik N. Akhtar, B. Southey, K. Porter, J. Sweedler, S. Rodriguez-Zas","doi":"10.1109/BIBMW.2011.6112530","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112530","url":null,"abstract":"Tools to identify proteins in tandem mass spectrometry experiments are not optimized to identify neuropeptides due to complex processing, post-translational modifications and neuropeptide size. The complementary strengths of three widely-used protein identification tools to identify neuropeptides were assessed. OMSSA, X!Tandem and Crux were applied to identify simulated mass spectra on a database of 7857 mouse neuropeptides from 92 prohormones. For each peptide, spectra was simulated with either +1, +2 and +3 precursor charge states, +1 charged b and y product ions having single water and/or ammonia loss depending on amino acid composition. OMSSA and X!Tandem identified 83% of the peptides with an E-value or P-value < 10−9, while Crux detected 81% and 11% of the peptides with a P-value < 10−1 and < 10−2, respectively. Precursor charge states have minor effect on the detection of neuropeptides. The sensitivity of either tool to detect small neuropeptides (< 10 amino acids in length) was limited. Our results suggest that methods optimized to detect neuropeptides are required.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"8 1","pages":"982-984"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85212518","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

Improved RNA-Seq Partitions in Linear Models for Isoform Quantification 改进的RNA-Seq划分线性模型用于异构体定量

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.102

Brian E. Howard, P. Veronese, S. Heber

引用次数: 0

Interaction simulation of Lipoxygenase with arachidonate acid using NAMD 用NAMD模拟脂氧合酶与花生四烯酸的相互作用

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112525

Zhongwei Li, E. Khosravi, Shuju Bai

引用次数: 0

Additive and multiplicative genome-wide association models identify genes associated with growth 加性和倍增性全基因组关联模型鉴定与生长相关的基因

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112527

Cynthia Zavala, N. Serao, M. Villamil, G. Caetano-Anollés, S. Rodriguez-Zas

引用次数: 0

Brdicka curve — A new source of biomarkers Brdicka曲线——生物标记物的新来源

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112374

Lenka Vyslouzilova, V. Adam, Andrea Szabóová, O. Štěpánková, R. Kizek, Jiří Anýž

引用次数: 1

An approach to phylogenomic analysis of bacterial pathogens 细菌性病原体的系统发育分析方法

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112529

L. Zaslavsky, V. Chetvernin, D. Dernovoy, B. Fedorov, W. Klimke, A. Souvorov, I. Tolstoy, T. Tatusova, D. Lipman

{"title":"An approach to phylogenomic analysis of bacterial pathogens","authors":"L. Zaslavsky, V. Chetvernin, D. Dernovoy, B. Fedorov, W. Klimke, A. Souvorov, I. Tolstoy, T. Tatusova, D. Lipman","doi":"10.1109/BIBMW.2011.6112529","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112529","url":null,"abstract":"From the beginning of the microbial genome sequencing era, researchers have shown a commendable commitment to phylogenetic diversity. The completion of one genome from each prokaryotic division or phylum is still a frequently articulated community goal. However, largely because of the interest in human pathogens and advances in sequencing technologies, there are also now a number of very closely related genomes whose organization and gene content can be directly compared. Studying genetic variability of pathogenic bacteria using whole-genome sequencing provides a way to understanding the mechanism of bacterial adaptation to rapid environmental changes and can be a source of useful information on virulence mechanisms. The bacterial genome datasets available in public archives represent a large collection of genome at different levels of sequence quality and assembly. A fast and reliable method of phylogenetic classification based on genome sequences provides a necessary foundation for a more detailed comparative analysis. NCBI has developed an approach of grouping bacterial organisms into phylogenetic clades using a genome dissimilarity measure based on the comparison of universally conserved markers. Special adjustments have been made to compensate for data inaccuracy and incompleteness. Tests performed on complete and draft genomes from phylum Proteobacteria demonstrated that the proposed robust genomic distance allows stable and reliable species-level clustering and can be used for forming phylogenetic clades. Since the tradeoff for the increased robustness of the method is its limited sensitivity at a very fine level, a phylogenomic refinement could be done within each constructed clade when file-level phylogenetic resolution of close genomes is necessary.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"16 1","pages":"981-981"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82713220","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 genetic algorithms for the inference of motifs that are represented in only a subset of sequences of interest 使用遗传算法对仅在感兴趣的序列子集中表示的基序进行推理

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112539

Jeffrey A. Thompson, C. Congdon

引用次数: 0

Multiobjective optizition shuffled frog-leaping biclustering 多目标优化洗牌蛙跃双聚类

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBMW.2011.6112368

Junwan Liu, Xiaohua Hu, Zhoujun Li, Yiming Chen

{"title":"Multiobjective optizition shuffled frog-leaping biclustering","authors":"Junwan Liu, Xiaohua Hu, Zhoujun Li, Yiming Chen","doi":"10.1109/BIBMW.2011.6112368","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112368","url":null,"abstract":"Biclustering of DNA microarray data that can mine significant patterns to help in understanding gene regulation and interactions. This is a classical multi-objective optimization problem (MOP). Recently, many researchers have developed stochastic search methods that mimic the efficient behavior of species such as ants, bees, birds and frogs, as a means to seek faster and more robust solutions to complex optimization problems. The particle swarm optimization(PSO) is a heuristics-based optimization approach simulating the movements of a bird flock finding food. The shuffled frog leaping algorithm (SFLA) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. This paper introduces SFL algorithm to solve biclustering of microarray data, and proposes a novel multi-objective shuffled frog leaping biclustering(MOSFLB) algorithm to mine coherent patterns from microarray data. Experimental results on two real datasets show that our approach can effectively find significant biclusters of high quality.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"46 1","pages":"151-156"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84430425","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: 6

Data Exploration in Secondary Use of Healthcare Data 医疗数据二次使用中的数据探索

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.129

Jian Wang

{"title":"Data Exploration in Secondary Use of Healthcare Data","authors":"Jian Wang","doi":"10.1109/BIBM.2011.129","DOIUrl":"https://doi.org/10.1109/BIBM.2011.129","url":null,"abstract":"Real world data sets (as opposed to data from randomized, controlled clinical trials) are becoming increasing available from the healthcare industry. Large databases from EMRs/EHRs, insurance claims, pharmacy records, disease registries etc present unique challenges when they are utilized to support pharmaceutical R&D activities. Such \"secondary use\" of healthcare data usually starts with an exploratory phase when the researcher takes a high-level view of the available data and starts to \"connect the dots\". Data exploration is a highly dynamic process: exploratory paths change frequently, sometimes converging, other times diverging, and often resulting in dead ends. Only a small subset of exploratory results end up being formally analyzed to derive quantitative insights. Because of this dynamic nature of data exploration, it is critical that researchers who generate hypotheses, the domain experts, can directly explore in the available data space. Data exploration on large healthcare data sets is often a bottleneck because these data sets tend to be poorly understood in terms of their quality, completeness, consistency, etc. We will discuss this emerging landscape, focusing on case studies to illustrate the powerful convergence of real-world data and technological advancements to help leverage this data.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"43 1","pages":"658-658"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90476152","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 Artificial Neural Network Based Approach for Identification of Native Protein Structures Using an Extended Forcefield 基于人工神经网络的扩展力场蛋白质结构识别方法

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Pub Date : 2011-11-12 DOI: 10.1109/BIBM.2011.53

T. M. Fawcett, S. Irausquin, Mikhail Simin, H. Valafar

引用次数: 2