Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112530
Malik N. Akhtar, B. Southey, K. Porter, J. Sweedler, S. Rodriguez-Zas
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.
由于复杂的加工、翻译后修饰和神经肽的大小，在串联质谱实验中鉴定蛋白质的工具尚未优化用于鉴定神经肽。评估了三种广泛使用的蛋白质鉴定工具用于鉴定神经肽的互补优势。OMSSA X !采用Tandem和Crux对92种原激素的7857种小鼠神经肽数据库进行模拟质谱鉴定。对于每个肽，用+1、+2和+3前体电荷状态模拟光谱，根据氨基酸组成，+1带电的b和y产物离子具有单一的水和/或氨损失。OMSSA和X!Tandem检测出83%的e值或p值< 10−9，而Crux分别检测出81%和11%的p值< 10−1和< 10−2的肽。前体电荷态对神经肽的检测影响较小。两种工具检测小神经肽(长度< 10个氨基酸)的灵敏度有限。我们的结果表明，需要优化检测神经肽的方法。
{"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}
Here, we present an extension of our is form quantification method that accommodates paired end RNA Sequencing data. We explore several alternate methods of partitioning read count data in order to better exploit the available fragment size distribution, and to reduce the variance in the resulting estimates. In many cases, this significantly improves the accuracy of our approach.
{"title":"Improved RNA-Seq Partitions in Linear Models for Isoform Quantification","authors":"Brian E. Howard, P. Veronese, S. Heber","doi":"10.1109/BIBM.2011.102","DOIUrl":"https://doi.org/10.1109/BIBM.2011.102","url":null,"abstract":"Here, we present an extension of our is form quantification method that accommodates paired end RNA Sequencing data. We explore several alternate methods of partitioning read count data in order to better exploit the available fragment size distribution, and to reduce the variance in the resulting estimates. In many cases, this significantly improves the accuracy of our approach.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"24 1","pages":"151-154"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85037371","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112525
Zhongwei Li, E. Khosravi, Shuju Bai
Lipoxygenase (LOX) family is believed as the major cause of pathological symptoms in asthma by biosynthesis of leukotrienes. The physiological function is known as firstly producing 8R-HPETE (derived from arachidonate acid, referred as AA), which is transformed in further enzymatic step into leukotrienes. However, much less detail is known about the role of 5-Lox in the inflammatory reaction. We have used the 1.85Å resolution structure of a wild coral Lipoxygenase (8R-LOX) (with 41% sequence identical to the human arachidonate 5-LOX) as a foundation to model the interactions between 8R-Lox and its substrate AA, and its binding site was identified using ICM. In this research, the 8R-Lox:AA complex obtained was refined and analyzed by molecular dynamic method (NAMD). Parameterization scheme for unknown structure of non-heme iron ligated by a series of residues was developed using VMD paratool plugin. All quantum mechanical calculation were performed by Gaussian03 with the Becke3LYP functional at 6–31G(d) basis set.
{"title":"Interaction simulation of Lipoxygenase with arachidonate acid using NAMD","authors":"Zhongwei Li, E. Khosravi, Shuju Bai","doi":"10.1109/BIBMW.2011.6112525","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112525","url":null,"abstract":"Lipoxygenase (LOX) family is believed as the major cause of pathological symptoms in asthma by biosynthesis of leukotrienes. The physiological function is known as firstly producing 8R-HPETE (derived from arachidonate acid, referred as AA), which is transformed in further enzymatic step into leukotrienes. However, much less detail is known about the role of 5-Lox in the inflammatory reaction. We have used the 1.85Å resolution structure of a wild coral Lipoxygenase (8R-LOX) (with 41% sequence identical to the human arachidonate 5-LOX) as a foundation to model the interactions between 8R-Lox and its substrate AA, and its binding site was identified using ICM. In this research, the 8R-Lox:AA complex obtained was refined and analyzed by molecular dynamic method (NAMD). Parameterization scheme for unknown structure of non-heme iron ligated by a series of residues was developed using VMD paratool plugin. All quantum mechanical calculation were performed by Gaussian03 with the Becke3LYP functional at 6–31G(d) basis set.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"34 1","pages":"973-973"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83635752","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112527
Cynthia Zavala, N. Serao, M. Villamil, G. Caetano-Anollés, S. Rodriguez-Zas
Standard genome-wide association studies evaluate the association between single nucleotide polymorphisms (SNPs or Genotype G) and phenotype (e.g. growth) conditional on non-SNP covariates including environmental factors (E, e.g. diet) or population stratification, on an additive fashion. For traits known to be the result of genotype-by-environment interactions (G×E), like growth, a multiplicative model could potentially uncover additional SNPs that influence growth on a context-dependent (e.g. diet or breed) fashion. The objective of this study was to assess and compare the performance of context-independent (additive, G+E) and context-dependent (multiplicative, G+E+G×E) models to identify polymorphisms and corresponding genes associated with growth that are context-independent and context-dependent. In addition to single-SNP analysis, a multi-SNP haplotype-based analysis that can increase the precision of the estimates was evaluated for the additive model.
{"title":"Additive and multiplicative genome-wide association models identify genes associated with growth","authors":"Cynthia Zavala, N. Serao, M. Villamil, G. Caetano-Anollés, S. Rodriguez-Zas","doi":"10.1109/BIBMW.2011.6112527","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112527","url":null,"abstract":"Standard genome-wide association studies evaluate the association between single nucleotide polymorphisms (SNPs or Genotype G) and phenotype (e.g. growth) conditional on non-SNP covariates including environmental factors (E, e.g. diet) or population stratification, on an additive fashion. For traits known to be the result of genotype-by-environment interactions (G×E), like growth, a multiplicative model could potentially uncover additional SNPs that influence growth on a context-dependent (e.g. diet or breed) fashion. The objective of this study was to assess and compare the performance of context-independent (additive, G+E) and context-dependent (multiplicative, G+E+G×E) models to identify polymorphisms and corresponding genes associated with growth that are context-independent and context-dependent. In addition to single-SNP analysis, a multi-SNP haplotype-based analysis that can increase the precision of the estimates was evaluated for the additive model.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"24 1","pages":"975-977"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85978997","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112374
Lenka Vyslouzilova, V. Adam, Andrea Szabóová, O. Štěpánková, R. Kizek, Jiří Anýž
This paper is devoted to analysis of voltammograms resulting from Brdicka reaction - the graphs that are currently used for determination of content of metallothioneins (MT) in tissue samples most often. We describe our search for typical patterns in the considered curves that would make it possible to distinguish among voltammograms produced by samples taken from different body parts. We suggest a rather compact representation of information contained in the considered graphs that is based on Haar's Simple Wavelet transformation. The resulting representation is successfully tested for classification of real data obtained from 8 rats and their 9 body parts. The preliminary experiments confirm that the suggested derived attributes of Brdicka curves seem to be good candidates for becoming numerical biomarkers exhibiting an important advantage: the process leading to their calculation can be fully automated.
{"title":"Brdicka curve — A new source of biomarkers","authors":"Lenka Vyslouzilova, V. Adam, Andrea Szabóová, O. Štěpánková, R. Kizek, Jiří Anýž","doi":"10.1109/BIBMW.2011.6112374","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112374","url":null,"abstract":"This paper is devoted to analysis of voltammograms resulting from Brdicka reaction - the graphs that are currently used for determination of content of metallothioneins (MT) in tissue samples most often. We describe our search for typical patterns in the considered curves that would make it possible to distinguish among voltammograms produced by samples taken from different body parts. We suggest a rather compact representation of information contained in the considered graphs that is based on Haar's Simple Wavelet transformation. The resulting representation is successfully tested for classification of real data obtained from 8 rats and their 9 body parts. The preliminary experiments confirm that the suggested derived attributes of Brdicka curves seem to be good candidates for becoming numerical biomarkers exhibiting an important advantage: the process leading to their calculation can be fully automated.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"11 1","pages":"193-198"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82506842","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112529
L. Zaslavsky, V. Chetvernin, D. Dernovoy, B. Fedorov, W. Klimke, A. Souvorov, I. Tolstoy, T. Tatusova, D. Lipman
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.
{"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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112539
Jeffrey A. Thompson, C. Congdon
In this work, we present GAMID, and extension of GAMI. GAMID is designed to be used for motif inference in noncoding DNA for co-expressed genes or for divergent species. In these cases, we would like to allow the inferred motif to be present in only a subset of the input data. This paper describes the approach and presents preliminary results.
{"title":"Using genetic algorithms for the inference of motifs that are represented in only a subset of sequences of interest","authors":"Jeffrey A. Thompson, C. Congdon","doi":"10.1109/BIBMW.2011.6112539","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112539","url":null,"abstract":"In this work, we present GAMID, and extension of GAMI. GAMID is designed to be used for motif inference in noncoding DNA for co-expressed genes or for divergent species. In these cases, we would like to allow the inferred motif to be present in only a subset of the input data. This paper describes the approach and presents preliminary results.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"14 1","pages":"1005-1005"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84363061","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112368
Junwan Liu, Xiaohua Hu, Zhoujun Li, Yiming Chen
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.
{"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}
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.
{"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}
T. M. Fawcett, S. Irausquin, Mikhail Simin, H. Valafar
Current protein force fields like the ones seen in CHARMM or Xplor-NIH have many terms that include bonded and non-bonded terms. Yet the force fields do not take into account the use of hydrogen bonds which are important for secondary structure creation and stabilization of proteins. SCOPE is an open-source program that generates proteins from rotamer space. It then creates a force field that uses only non-bonded and hydrogen bond energy terms to create a profile for a given protein. The profiles can then be used in an artificial neural network to create a linear model which is funneled to the true protein conformation.
{"title":"An Artificial Neural Network Based Approach for Identification of Native Protein Structures Using an Extended Forcefield","authors":"T. M. Fawcett, S. Irausquin, Mikhail Simin, H. Valafar","doi":"10.1109/BIBM.2011.53","DOIUrl":"https://doi.org/10.1109/BIBM.2011.53","url":null,"abstract":"Current protein force fields like the ones seen in CHARMM or Xplor-NIH have many terms that include bonded and non-bonded terms. Yet the force fields do not take into account the use of hydrogen bonds which are important for secondary structure creation and stabilization of proteins. SCOPE is an open-source program that generates proteins from rotamer space. It then creates a force field that uses only non-bonded and hydrogen bond energy terms to create a profile for a given protein. The profiles can then be used in an artificial neural network to create a linear model which is funneled to the true protein conformation.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"15 1","pages":"500-505"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89766963","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}
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