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A direct comparison of protein interaction confidence assignment schemes

BMC bioinformatics 2006年第1期7卷 360-360页

作者： Suthram, Silpa Shlomi, Tomer Ruppin, Eytan Sharan, Roded Ideker, Trey Department of Bioengineering University of California San Diego CA 92093 USA Program in Bioinformatics University of California San Diego CA 92093 USA School of Computer Science Tel-Aviv University Tel Aviv 69978 Israel

Background: Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing large protein interaction networks for various species at an ever-growing pace. However, common technologies like yeast two-hybrid may experience high rates of false positive detection. To combat false positive discoveries, a number of different methods have been recently developed that associate confidence scores with protein interactions. Here, we perform a rigorous comparative analysis and performance assessment among these different methods. Results: We measure the extent to which each set of confidence scores correlates with similarity of the interacting proteins in terms of function, expression, pattern of sequence conservation, and homology to interacting proteins in other species. We also employ a new metric, the Signal-to-Noise Ratio of protein complexes embedded in each network, to assess the power of the different methods. Seven confidence assignment schemes, including those of Bader et al., Deane et al., Deng et al., Sharan et al., and Qi et al., are compared in this work. Conclusion: Although the performance of each assignment scheme varies depending on the particular metric used for assessment, we observe that Deng et al. yields the best performance overall ( in three out of four viable measures). Importantly, we also find that utilizing any of the probability assignment schemes is always more beneficial than assuming all observed interactions to be true or equally likely.

关键词： Gene Ontology Protein Interaction Network Assignment Scheme Probability Scheme Yeast Interaction

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Searching for ribosome-mRNA hybridization sites

Searching for ribosome-mRNA hybridization sites

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： J. Starmer A. Stomp M. Vouk D. Bitzer Bioinformatics Program North Carolina State University USA Department of Forestry North Carolina State University USA Department of Computer Science North Carolina State University USA

In prokaryotes, Shine-Dalgarno (SD) sequences, nucleotides upstream from start codons on messenger RNAs (mRNAs) that are complementary to ribosomal RNA (rRNA), facilitate the initiation of protein synthesis by allowing the two molecules to hybridize and form a double-helix. Many mRNAs, however, lack SD sequences and presumably use another mechanism to recruit ribosomes. We define a hybridization site (HS) as any mRNA sequence that forms as strong a bond to rRNA as a minimal SD sequence. Thus, SD sequences are a class of HSs that occur in a specific region. We scanned the translation initiation regions (TIRs) in 18 prokaryotic genomes and found that only 56% of the TIRs contained SD sequences, and the most stable HSs are not always upstream from start codons. Indeed, we found 2407 genes where the strongest HS in the TIR includes the start codon, deviating from the expected location of 5 to 10 bases upstream. This result suggests an alternative sequence for recruiting ribosomes to mRNAs that has yet to be characterized.

关键词： Tail RNA bioinformatics Recruitment Genomics Bonding Forestry computer science Proteins Region 5

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A Supervised Constructive Neuro-Immune Network for Pattern Classification

A Supervised Constructive Neuro-Immune Network for Pattern C...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Knidel L. Nunes de Castro F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program in Computer Science Catholic University of Santos Sao Paulo Brazil

This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.

关键词： Pattern classification Immune system Neurons Supervised learning Vector quantization Classification algorithms Prototypes Clustering algorithms Unsupervised learning Artificial neural networks

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Identifying interaction sites in "Recalcitrant" proteins: Predicted protein and RNA binding sites in REV proteins of HIV-1 and EIAV agree with experimental data

Identifying interaction sites in "Recalcitrant" proteins: Pr...

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11th Pacific Symposium on Biocomputing 2006, PSB 2006

作者： Terribllini, Michael Lee, Jae-Hyung Yan, Changhui Jernigan, Robert L. Carpenter, Susan Honavar, Vasant Dobbs, Drena Bioinformatics and Computational Biology Graduate Program LH. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50010 United States Department of Computer Science Iowa State University Ames IA 50010 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50010 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50010 United States Artificial Intelligence Research Laboratory Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA 50010 United States Department of Veterinary Microbiology and Pathology Washington State University Pullman WA. 99164 United States

ISBN: (纸本)9812564632

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation or gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the proiein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-I and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.

关键词： Binding sites

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Multiple alignment of continuous time series

Multiple alignment of continuous time series

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18th Annual Conference on Neural Information Processing Systems, NIPS 2004

作者： Listgarten, Jennifer Neal, Radford M. Roweis, Sam T. Emili, Andrew Department of Computer Science University of Toronto Toronto ON M5S 3G4 Canada Banting and Best Department of Medical Research Program in Proteomics and Bioinformatics University of Toronto Toronto ON M5S 3G4 Canada

ISBN: (纸本)0262195348

Multiple realizations of continuous-valued time series from a stochastic process often contain systematic variations in rate and amplitude. To leverage the information contained in such noisy replicate sets, we need to align them in an appropriate way (for example, to allow the data to be properly combined by adaptive averaging). We present the Continuous Profile Model (CPM), a generative model in which each observed time series is a non-uniformly subsampled version of a single latent trace, to which local rescaling and additive noise are applied. After unsupervised training, the learned trace represents a canonical, high resolution fusion of all the replicates. As well, an alignment in time and scale of each observation to this trace can be found by inference in the model. We apply CPM to successfully align speech signals from multiple speakers and sets of Liquid Chromatography-Mass Spectrometry proteomic data.

关键词： Time series

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Protein function classification based on gene ontology

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2nd Asia Information Retrieval Symposium, AIRS 2005

作者： Park, Dae-Won Heo, Hyoung-Sam Kwon, Hyuk-Chul Chung, Hea-Young Department of Computer Science and Engineering Pusan National University 30 Jangjeon-dong Geumjung-gu Busan 609-735 Korea Republic of Interdisciplinary Program of Bioinformatics Pusan National University 30 Jangjeon-dong Geumjung-gu Busan 609-735 Korea Republic of Department of Pharmacy Pusan National University 030 Jangjeon-dong Geumjung-gu Busan 609-735 Korea Republic of

ISBN: (纸本)3540291865

Most proteins interact with other proteins, cells, tissues or diseases. They have biological functions and can be classified according to their functions. With the functions and the functional relations of proteins, we can explain many biological phenomena and obtain answers in solving biological problems. Therefore, it is important to determine the functions of proteins. In this paper we present a protein function classification method for the function prediction of proteins. With human proteins assigned to GO molecular function terms, we measure the similarity of proteins to function classes using the functional distribution. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Proteins

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bioinformatics in the undergraduate curriculum: Opportunities for computer science educators

Bioinformatics in the undergraduate curriculum: Opportunitie...

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Proceedings of the Thirty-Fifth SIGCSE Technical Symposium on computer science Education

作者： Burhans, Debra T. DeJongh, Matt Doom, Travis E. LeBlanc, Mark Computer Science Department Bioinformatics Program Canisius College Buffalo NY Computer Science Department Hope College Holland MI Computer Science Department BioMedical Science Program Wright State University Dayton OH Department of Math Wheaton College Norton MA

ISBN: (纸本)1581137982

Biology has become an increasingly data-driven science. Modern experimental techniques, including automated DNA sequencing, gene expression micro arrays, and X-ray crystallography are producing molecular data at a rate that has made traditional data analysis methods impractical. Computational methods are becoming an increasingly important aspect of the evaluation and analysis of experimental data in molecular biology. bioinformatics is the term coined for the new field that merges biology and computer science to manage and analyze this data, with the ultimate goal of understanding and modeling living systems. The emergence of bioinformatics provides new challenges and opportunities for computer science educators. This panel assembles four individuals who collectively have experience teaching bioinformatics at both liberal arts colleges and universities, and who also have industry experience in bioinformatics, to discuss various approaches to incorporating bioinformatics into the undergraduate curriculum.

关键词： computer science

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bioinformatics

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IEEE Potentials 2004年第1期23卷 24-27页

作者： Doom, Travis Raymer, Michael Krane, Dan IEEE Wright State University United States Department of Computer Science and Engineering Graduate Faculty in Biomedical Sciences Dayton OH United States WSU Biomedical Sciences Ph.D. Program Forensic Bioinformatics Inc. United States Bioinformatics Research Group Computer Science Department Wright State University United States

No abstract available

关键词： bioinformatics Biology computing Biological information theory Data analysis Computational biology Proteins Biochemistry Sequences Genomics Large-scale systems

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Multiple alignment of continuous time series 04

Multiple alignment of continuous time series

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Proceedings of the 18th International Conference on Neural Information Processing Systems

作者： Jennifer Listgarten Radford M. Neal Sam T. Roweis Andrew Emili Department of Computer Science University of Toronto Toronto Ontario Banting and Best Department of Medical Research and Program in Proteomics and Bioinformatics University of Toronto Toronto Ontario

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Prediction of the risk types of human papillomaviruses by Support Vector Machines

Prediction of the risk types of human papillomaviruses by Su...

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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence

作者： Joung, Je-Gun Sok June, O. Zhang, Byoung-Tak Biointelligence Laboratory Graduate Program in Bioinformatics Seoul National University Seoul 151-742 Korea Republic of Seoul National University Seoul 151-742 Korea Republic of School of Computer Science and Engineering Seoul National University Seoul 151-742 Korea Republic of Department of Pharmacology College of Medicine Inje University Busan 614-735 Korea Republic of

ISBN: (纸本)3540228179

Infection by high-risk human papillomaviruses (HPVs) is associated with the development of cervical cancers. Classification of risk types is important to understand the mechanisms in infection and to develop novel instruments for medical examination such as DNA microarrays. In this paper, we classify the risk type of HPVs by using the protein sequences. Our approach is based on the hidden Markov model and the Support Vector Machines. The former searches informative subsequence positions and the latter computes efficiently to classify protein sequences. In the experiments, the proposed classifier was compared with previous methods in accuracy and F-cost, also the prediction result of four unknown types is presented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Viruses

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