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Congress on Evolutionary Computation

作者： Kelvin Xi Zhang B.F. Francis Ouellette Ontario Institute for Cancer Research Toronto ONT Canada Graduate Program in Bioinformatics University of British Columbia BC Canada

Protein-protein interactions (PPIs) play an extremely important role in performing a variety of biological functions. The interactomes of several model organisms including budding yeast Saccharomyces cerevisiae have recently been studied using experimental techniques such as the yeast two-hybrid assay. However, these techniques are generally biased against integral membrane proteins due to their intrinsic limitations. Given the fact that the interactions between integral membrane proteins cover a large fraction of the whole interactome, we report a study of predicting interactions between integral membrane proteins in yeast by a quantitative model We integrate protein-protein interaction and domain-domain interaction (DDI) data from disparate sources and apply a log likelihood scoring method on all putative integral membrane proteins in yeast to predict their interactions based on a cut-off threshold. We show that our approach improves on other predictive approaches when tested on a ldquogold-standardrdquo data set and achieves 74.6% true positive rate at the expense of 0.43% false positive rate. Furthermore, we find that two integral membrane proteins are more likely to interact with each other if they share more common interaction partners. This study allows us to reach a more extensive understanding of the yeast integral membrane proteins from a network view, which also complements the previous prediction approaches based on the genomic context.

关键词： Evolutionary computation

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It takes glue to tango: MeDICi integration framework's data-intensive computing pipeline

引用

Scientific Computing 2008年第7期25卷 16-19页

作者： Gorton, Ian Oehmen, Christopher S. McDermott, Hason E. PNNL's Data Intensive Computing Program United States Laboratory's Bioinformatics and Computational Biology Group Fundamental and Computational Sciences Directorate United States

Researchers at the Department of Energy's (DOE) Pacific Northwest National Laboratory (PNNL) in Richland, WA, are creating computing environments for biologists that seamlessly integrate collections of data and computational resources. MeDICi is an evolving middleware platform for building complex, high-performance analytical applications. MIF components are constructed using Java programming interfaces that support inter-component communication using asynchronous messaging. Local components execute inside the MIF container. Remote components create distributed solutions and integrate with non-Java code. Mule provides the MIF container environment. MIF extends the Mule interface to make component and pipeline construction easier and to create an encapsulation device for component creation. The MIF interface is agnostic of the underlying Java messaging platform. This allows deployments to configure MIF applications using technologies that meet individual quality-of-service requirements.

关键词： Containers

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Knowledge-based versus experimentally acquired distance and angle constraints for NMR structure refinement

引用

Journal of bioinformatics and Computational Biology 2008年第2期6卷 283-300页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Laboratory of Cell Biology National Cancer Institute National Institutes of Health Bethesda MD 20892 United States Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Iowa State University Ames IA 50011 United States

Nuclear Overhauser effects (NOE) distance constraints and torsion angle constraints are major conformational constraints for nuclear magnetic resonance (NMR) structure refinement. In particular, the number of NOE constraints has been considered as an important determinant for the quality of NMR structures. Of course, the availability of torsion angle constraints is also critical for the formation of correct local conformations. In our recent work, we have shown how a set of knowledge-based short-range distance constraints can also be utilized for NMR structure refinement, as a complementary set of conformational constraints to the NOE and torsion angle constraints. In this paper, we show the results from a series of structure refinement experiments by using different types of conformational constraints - NOE, torsion angle, or knowledge-based constraints - or their combinations, and make a quantitative assessment on how the experimentally acquired constraints contribute to the quality of structural models and whether or not they can be combined with or substituted by the knowledge-based constraints. We have carried out the experiments on a small set of NMR structures. Our preliminary calculations have revealed that the torsion angle constraints contribute substantially to the quality of the structures, but require to be combined with the NOE constraints to be fully effective. The knowledge-based constraints can be functionally as crucial as the torsion angle constraints, although they are statistical constraints after all and are not meant to be able to replace the latter. © 2008 Imperial College Press.

关键词： Knowledge-based distance constraints NMR structure refinement NOE distance constraints Precision and accuracy of NMR structures Torsion angle constraints

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Fuzzy C-mean algorithm based on “complete” Mahalanobis distances

Fuzzy C-mean algorithm based on “complete” Mahalanobis dis...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hsiang-Chuan Liu Jeng-Ming Yih Der-Bang Wu Shin-Wu Liu Department of Bioinformatics Asia University Taichung Taiwan Department of Mathematics Education Taichung University 40306 Taiwan Program of Cell and Developmental Biology Rutgers University Piscataway NJ USA

The well known fuzzy partition clustering algorithms are most based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm, were developed to detect non-spherical structural clusters, but both of them based on semi-supervised Mahalanobis distance, these two algorithms fail to consider the relationships between cluster centers in the objective function, needing additional prior information. When some training cluster size is small than its dimensionality, it induces the singular problem of the inverse covariance matrix. It is an important issue. In our previous study, we developed a new unsupervised algorithm, FCM-M, to solve the singular problem of the inverse covariance matrix. But, the previous work only consider the local covariance matrix of each cluster. In this paper, an improved new unsupervised algorithm, ldquofuzzy c-mean based on complete Mahalanobis distance without any prior information (FCM-CM)rdquo, is proposed. The proposed new algorithm which is considered not only the local covariance matrix of each cluster but also the overall covariance matrix, which can get more information and higher accuracy by considering the overall covariance matrix. A real data set was applied to prove that the performance of the FCM-CM algorithm is better than those of the traditional FCM algorithm and our previous FCM-M. For choosing the better initial value of the same new algorithm, FCM-CM, the ratio method is still the best of all choosing methods by using in FCM-M and FCM algorithms in our previous works.

关键词： Clustering algorithms Covariance matrix Distance measurement Partitioning algorithms Artificial neural networks Machine learning algorithms Integrated circuits

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An information theoretic framework for genomic data analysis

An information theoretic framework for genomic data analysis

引用

IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Aaron McKenna Gil Alterovitz Bioinformatics Program Boston University Boston MA USA Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Children's Hospital Informatics Program Boston MA

The breadth of biological data collected in the last decade has far outstripped the methods available to process it. To effectively investigate and explore this abundance of data, novel automated collection and analysis approaches must be devised. We have developed a new open software framework, the Open Genomic Analysis Platform (OGAP), to aid in the analysis of genomic data. It is capable of analyzing a variety of data source, and focuses on using information theory to characterize data. The frameworks has is capable of import a variety of genome tied data, and provides custom analysis and visualization of results. We then demonstrate the use of this framework analyzing the Prochlorococcus Marinus organism. We show a strong correlation between the information content of sequence data and up regulation of gene expression during lytic infection.

关键词： Genomics bioinformatics Data analysis Biological information theory Open source software Information analysis Information theory Data visualization Organisms Gene expression

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bioinformatics tools for plant genomics

引用

International Journal of Plant Genomics 2008年第unknown期2008卷 910474页

作者： Skuse, Gary R. Du, Chunguang Bioinformatics Program Department of Biological Sciences Rochester Institute of Technology Rochester NY 14623 United States Science Informatics Program Department of Biology and Molecular Biology Montclair State University NJ 07043 Montclair United States

Progress and interest in plant genomics have been accelerating since the time in late 2000 when the genome of Arabidopsis thaliana was published. [...] many genome sequencing projects have been undertaken that include poplar (Populus), grape (Vitis), the moss Physcomitrella, the biflagellate algae Chlamydomonas and several globally crucial crop plants such as corn (Maize) and rice (Oryza).

关键词：

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Developmental systems biology flourishing on new technologies

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Journal of Genetics and Genomics 2008年第10期35卷 577-584页

作者： Jing-Dong J. Han Yi Liu Huiling Xue Kai Xia Hong Yu Shanshan Zhu Zhang Chen Wei Zhang Zheng Huang Chunyu Jin Bo Xian Jing Li Lei Hou Yixing Han Chaoqun Niu Timothy C. Alcon Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology Center for Molecular Systems Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China Key Laboratory of Machine Perception （Ministry of Education） Peking University Beijing 100871 China Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA 50011-1040 USA

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems Biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

关键词： developmental systems biology new technologies symposium

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Combining Hierarchical Inference in Ontologies with Heterogeneous Data Sources Improves Gene Function Prediction

Combining Hierarchical Inference in Ontologies with Heteroge...

引用

IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Xiaoyu Jiang Naoki Nariai Martin Steffen Simon Kasif David Gold Eric D. Kolaczyk Department of Mathematics and Statistics Boston University Boston MA USA Bioinformatics Program Boston University Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA Boston University Boston MA US

The study of gene function is critical in various genomic and proteomic fields. Due to the availability of tremendous amounts of different types of protein data, integrating these datasets to predict function has become a significant opportunity in computational biology. In this paper, to predict protein function we (i) develop a novel Bayesian framework combining relational,hierarchical and structural information with improvement in data usage efficiency over similar methods, and (ii) propose to use it in conjunction with an integrative protein-protein association network, STRING (Search Tool for the Retrieval of INteracting Genes/proteins), which combines information from seven different sources. At the heart of our work is accomplishing protein data integration in a concerted fashion with respect to algorithm and data source. Method performance is assessed by a 5-fold cross-validation in yeast on selected terms from the Molecular Function ontology in the Gene Ontology database. Results show that our combined use of the proposed computational framework and the protein network from STRING offers substantial improvements in prediction. The benefits of using an aggressively integrative network, such as STRING, may derive from the fact that although it is likely that the ultimate gene interaction matrix (including but not limited to protein-protein, genetic, or regulatory interactions) will be sparse, presently it is still known only incompletely in most organisms, and thus the use of multiple distinct data sources is rewarded.

关键词： Ontologies Protein engineering Genomics bioinformatics Proteomics Computational biology Bayesian methods Information retrieval Heart Fungi

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Using a literature-based NMF model for discovering gene functional relationships

Using a literature-based NMF model for discovering gene func...

引用

IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Elina Tjioe Michael Berry Ramin Homayouni Genome Science and Technology Graduate School University of Tennessee Knoxville TN USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Bioinformatics Program Department of Biology University of Memphis Memphis TN USA

The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. In this study, we develop a Web-based bioinformatics software environment called FAUN or feature annotation using nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. We tested FAUN on three manually constructed gene document collections, and then used it to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g., MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation.

关键词： bioinformatics Genomics Data mining Matrix decomposition Large scale integration Feature extraction Iterative methods Biological system modeling Computer science Computational biology

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引用

13th Pacific Symposium on Biocomputing, PSB 2008

作者： Lee, Jae-Hyung Hamilton, Michael Gleeson, Colin Caragea, Cornelia Zaback, Peter Sander, Jeffry D. Li, Xue Wu, Feihong Terribilini, Michael Honavar, Vasant Dobbs, Drena Bioinformatics and Computational Biology Program L.H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50010 United States Dept. of Genetics Development and Cell Biology Discovery Iowa State University Ames IA 50010 United States Dept. of Computer Science Iowa State University Ames IA 50010 United States Artificial Intelligence Research Lab Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA 50010 United States Dept. of Computer Science Colorado State University Fort Collins CO 80523 United States Dept. of Biological Sciences Univ. of Illinois Chicago IL 60607 United States

ISBN: (纸本)9812776087

Telomerase is a ribonucleoprotein enzyme that adds telomeric DNA repeat sequences to the ends of linear chromosomes. The enzyme plays pivotal roles in cellular senescence and aging, and because it provides a telomere maintenance mechanism for ∼90% of human cancers, it is a promising target for cancer therapy. Despite its importance, a high-resolution structure of the telomerase enzyme has been elusive, although a crystal structure of an N-terminal domain (TEN) of the telomerase reverse transcriptase subunit (TERT) from Tetrahymena has been reported. In this study, we used a comparative strategy, in which sequence-based machine learning approaches were integrated with computational structural modeling, to explore the potential conservation of structural and functional features of TERT in phylogenetically diverse species. We generated structural models of the N-terminal domains from human and yeast TERT using a combination of threading and homology modeling with the Tetrahymena TEN structure as a template. Comparative analysis of predicted and experimentally verified DNA and RNA binding residues, in the context of these structures, revealed significant similarities in nucleic acid binding surfaces of Tetrahymena and human TEN domains. In addition, the combined evidence from machine learning and structural modeling identified several specific amino acids that are likely to play a role in binding DNA or RNA, but for which no experimental evidence is currently available. © 2008 World Scientific Publishing Co. Pte. Ltd.

关键词： Enzymes

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