Proceedings. IEEE Computational Systems Bioinformatics Conference最新文献_第2页

Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching. 基于马尔可夫切换的动态线性模型估计时间序列微阵列数据的时变基因网络。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.32

Ryo Yoshida, Seiya Imoto, Tomoyuki Higuchi

引用次数: 43

Learning yeast gene functions from heterogeneous sources of data using hybrid weighted Bayesian networks. 利用混合加权贝叶斯网络从异构数据源学习酵母基因功能。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.38

Xutao Deng, Huimin Geng, Hesham Ali

{"title":"Learning yeast gene functions from heterogeneous sources of data using hybrid weighted Bayesian networks.","authors":"Xutao Deng, Huimin Geng, Hesham Ali","doi":"10.1109/csb.2005.38","DOIUrl":"https://doi.org/10.1109/csb.2005.38","url":null,"abstract":"We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or ORFs (Open Reading Frames) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore many functional links would be missing when only one or two source of data is used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.","PeriodicalId":87417,"journal":{"name":"Proceedings. IEEE Computational Systems Bioinformatics Conference","volume":" ","pages":"25-34"},"PeriodicalIF":0.0,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2005.38","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25829586","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: 8

Analysis of SNP-expression association matrices. snp表达关联矩阵分析。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.14

Anya Tsalenko, Roded Sharan, Hege Edvardsen, Vessela Kristensen, Anne-Lise Børresen-Dale, Amir Ben-Dor, Zohar Yakhini

引用次数: 4

Identification of post-translational modifications via blind search of mass-spectra. 通过质谱盲搜索鉴定翻译后修饰。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.34

Dekel Tsur, Stephen Tanner, Ebrahim Zandi, Vineet Bafna, Pavel A Pevzner

{"title":"Identification of post-translational modifications via blind search of mass-spectra.","authors":"Dekel Tsur, Stephen Tanner, Ebrahim Zandi, Vineet Bafna, Pavel A Pevzner","doi":"10.1109/csb.2005.34","DOIUrl":"https://doi.org/10.1109/csb.2005.34","url":null,"abstract":"Post-translational modifications (PTMs) are of great biological importance. Most existing approaches perform a restrictive search that can only take into account a few types of PTMs and ignore all others. We describe an unrestrictive PTM search algorithm that searches for all types of PTMs at once in a blind mode, i.e., without knowing which PTMs exist in a sample. The blind PTM identification opens a possibility to study the extent and frequencies of different types of PTMs, still an open problem in proteomics. Using our new algorithm, we were able to construct a two-dimensional PTM frequency matrix that reflects the number of MS/MS spectra in a sample for each putative PTM type and each amino acid. Application of this approach to a large IKKb dataset resulted in the largest set of PTMs reported for a single MS/MS sample so far. We demonstrate an excellent correlation between high values in the PTM frequency matrix and known PTMs thus validating our approach. We further argue that the PTM frequency matrix may reveal some still unknown modifications that warrant further experimental validation.","PeriodicalId":87417,"journal":{"name":"Proceedings. IEEE Computational Systems Bioinformatics Conference","volume":" ","pages":"157-66"},"PeriodicalIF":0.0,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2005.34","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25829942","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: 51

A topological measurement for weighted protein interaction network. 加权蛋白质相互作用网络的拓扑测量方法。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.8

Pengjun Pei, Aidong Zhang

引用次数: 39

Application of a generalized MWC model for the mathematical simulation of metabolic pathways regulated by allosteric enzymes. 应用广义MWC模型对变构酶调控的代谢途径进行数学模拟。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.15

Tarek S Najdi, Chin-Rang Yang, Bruce E Shapiro, G Wesley Hatfield, Eric D Mjolsness

{"title":"Application of a generalized MWC model for the mathematical simulation of metabolic pathways regulated by allosteric enzymes.","authors":"Tarek S Najdi, Chin-Rang Yang, Bruce E Shapiro, G Wesley Hatfield, Eric D Mjolsness","doi":"10.1109/csb.2005.15","DOIUrl":"https://doi.org/10.1109/csb.2005.15","url":null,"abstract":"In our effort to elucidate the systems biology of the model organism, Escherichia coli, we have developed a mathematical model that simulates the allosteric regulation for threonine biosynthesis pathway starting from aspartate. To achieve this goal, we used kMech, a Cellerator language extension that describes enzyme mechanisms for the mathematical modeling of metabolic pathways. These mechanisms are converted by Cellerator into ordinary differential equations (ODEs) solvable by Mathematica. In this paper, we describe a more flexible model in Cellerator, which generalizes the Monod, Wyman, Changeux (MWC) model for enzyme allosteric regulation to allow for multiple substrate, activator and inhibitor binding sites. Furthermore, we have developed a model that describes the behavior of the bifunctional allosteric enzyme aspartate Kinase I-Homoserine Dehydrogenase I (AKI-HDHI). This model predicts the partition of enzyme activities in the steady state which paves a way for a more generalized prediction of the behavior of bifunctional enzymes.","PeriodicalId":87417,"journal":{"name":"Proceedings. IEEE Computational Systems Bioinformatics Conference","volume":" ","pages":"279-88"},"PeriodicalIF":0.0,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2005.15","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25829324","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: 18

Identifying simple discriminatory gene vectors with an information theory approach. 用信息论方法识别简单的歧视性基因载体。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.35

Zheng Yun, Kwoh Chee Keong

{"title":"Identifying simple discriminatory gene vectors with an information theory approach.","authors":"Zheng Yun, Kwoh Chee Keong","doi":"10.1109/csb.2005.35","DOIUrl":"https://doi.org/10.1109/csb.2005.35","url":null,"abstract":"In the feature selection of cancer classification problems, many existing methods consider genes individually by choosing the top genes which have the most significant signal-to-noise statistic or correlation coefficient. However the information of the class distinction provided by such genes may overlap intensively, since their gene expression patterns are similar. The redundancy of including many genes with similar gene expression patterns results in highly complex classifiers. According to the principle of Occam's razor, simple models are preferable to complex ones, if they can produce comparable prediction performances to the complex ones. In this paper, we introduce a new method to learn accurate and low-complexity classifiers from gene expression profiles. In our method, we use mutual information to measure the relation between a set of genes, called gene vectors, and the class attribute of the samples. The gene vectors are in higher-dimensional spaces than individual genes, therefore, they are more diverse, or contain more information than individual genes. Hence, gene vectors are more preferable to individual genes in describing the class distinctions between samples since they contain more information about the class attribute. We validate our method on 3 gene expression profiles. By comparing our results with those from literature and other well-known classification methods, our method demonstrated better or comparable prediction performances to the existing methods, however, with lower-complexity models than existing methods.","PeriodicalId":87417,"journal":{"name":"Proceedings. IEEE Computational Systems Bioinformatics Conference","volume":" ","pages":"13-24"},"PeriodicalIF":0.0,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2005.35","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25829583","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: 16

A learned comparative expression measure for affymetrix genechip DNA microarrays. affymetrix基因芯片DNA微阵列的学习比较表达测量。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.5

Will Sheffler, Eli Upfal, John Sedivy, William Stafford Noble

{"title":"A learned comparative expression measure for affymetrix genechip DNA microarrays.","authors":"Will Sheffler, Eli Upfal, John Sedivy, William Stafford Noble","doi":"10.1109/csb.2005.5","DOIUrl":"https://doi.org/10.1109/csb.2005.5","url":null,"abstract":"Perhaps the most common question that a microarray study can ask is, \"Between two given biological conditions, which genes exhibit changed expression levels?\" Existing methods for answering this question either generate a comparative measure based upon a static model, or take an indirect approach, first estimating absolute expression levels and then comparing the estimated levels to one another. We present a method for detecting changes in gene expression between two samples based on data from Affymetrix GeneChips. Using a library of over 200,000 known cases of differential expression, we create a learned comparative expression measure (LCEM) based on classification of probe-level data patterns as changed or unchanged. LCEM uses perfect match probe data only; mismatch probe values did not prove to be useful in this context. LCEM is particularly powerful in the case of small microarry studies, in which a regression-based method such as RMA cannot generalize, and in detecting small expression changes. At the levels of selectivity that are typical in microarray analysis, the LCEM shows a lower false discovery rate than either MAS5 or RMA trained from a single chip. When many chips are available to RMA, LCEM performs better on two out of the three data sets, and nearly as well on the third. Performance of the MAS5 log ratio statistic was notably bad on all datasets.","PeriodicalId":87417,"journal":{"name":"Proceedings. IEEE Computational Systems Bioinformatics Conference","volume":" ","pages":"144-54"},"PeriodicalIF":0.0,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2005.5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25829941","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: 8

Accurate prediction of orthologous gene groups in microbes. 微生物中同源基因群的准确预测。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.10

Hongwei Wu, Fenglou Mao, Victor Olman, Ying Xu

引用次数: 7

Multi-metric and multi-substructure biclustering analysis for gene expression data. 基因表达数据的多度量和多亚结构双聚类分析。

Proceedings. IEEE Computational Systems Bioinformatics Conference Pub Date : 2005-01-01 DOI: 10.1109/csb.2005.40

S Y Kung, Man-Wai Mak, Ilias Tagkopoulos

{"title":"Multi-metric and multi-substructure biclustering analysis for gene expression data.","authors":"S Y Kung, Man-Wai Mak, Ilias Tagkopoulos","doi":"10.1109/csb.2005.40","DOIUrl":"https://doi.org/10.1109/csb.2005.40","url":null,"abstract":"A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluster. We shall show that almost all matrix metrics can be converted into vector norms while preserving the rank equivalence. Vector norms provide a much more efficient vehicle for biclustering analysis and computation. The advantages are two folds: ease of analysis and saving of computation. Most existing biclustering algorithms have also implicitly assumed the use of univariate (i.e., single metric) evaluation for identifying biclusters. Such an approach however overlooks the fundamental principle that genes (even though they may belong to the same gene group) (1) may be subdivided into different substructures; and (2) they may be co-expressed via a diversity of coherence models (a gene may participate in multiple pathways that may or may not be co-active under all conditions). The former leads to the adoption of a multi-substurcture analysis, while the latter to the multivariate analysis. This paper will show that the proposed multivariate and multi-subscluster analysis is very effective in identifying and classifying biologically relevant groups in genes and conditions. For example, it has successfully yielded highly discriminant and accurate classification based on known ribosomal gene groups.","PeriodicalId":87417,"journal":{"name":"Proceedings. IEEE Computational Systems Bioinformatics Conference","volume":" ","pages":"123-34"},"PeriodicalIF":0.0,"publicationDate":"2005-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/csb.2005.40","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25829997","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: 11