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Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification

BIOINFORMATICS 2015年第9期31卷 1396-1404页

作者： Borozan, Ivan Watt, Stuart Ferretti, Vincent MaRS Ctr Ontario Inst Canc Res Dept Informat & Biocomp Toronto ON Canada

Motivation: Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. Results: Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Because the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures, we show that our model significantly improves the classification accuracy over the current composition- and alignment-based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences.

关键词： Algorithms classification/methods DNA, Viral Metagenomics Models, Theoretical Sequence Alignment Sequence Analysis, DNA/methods Sequence Analysis, Protein/methods Viruses/classification

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The Use and Misuse of Psychometric Models

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PSYCHOMETRIKA 2014年第2期79卷 347-354页

作者： Templin, Jonathan Bradshaw, Laine Univ Kansas Lawrence KS 66045 USA Univ Georgia Athens GA 30602 USA

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Practical implementation of an existing smoking detection pipeline and reduced support vector machine training corpus requirements

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2014年第1期21卷 27-30页

作者： Khor, Richard Yip, Wai-Kuan Bressel, Mathias Rose, William Duchesne, Gillian Foroudi, Farshad Peter MacCallum Canc Ctr Dept Radiat Oncol Melbourne Vic 8006 Australia Univ Melbourne Sir Peter MacCallum Dept Oncol Melbourne Vic Australia Ctr Biostat & Clin Trials Melbourne Vic Australia Peter MacCallum Canc Ctr Dept Informat Management Melbourne Vic 8006 Australia Monash Univ Dept Biochem Melbourne Vic 3004 Australia Monash Univ Dept Radiat Med Melbourne Vic 3004 Australia

This study aimed to reduce reliance on large training datasets in support vector machine (SVM)-based clinical text analysis by categorizing keyword features. An enhanced Mayo smoking status detection pipeline was deployed. We used a corpus of 709 annotated patient narratives. The pipeline was optimized for local data entry practice and lexicon. SVM classifier retraining used a grouped keyword approach for better efficiency. Accuracy, precision, and F-measure of the unaltered and optimized pipelines were evaluated using k-fold cross-validation. Initial accuracy of the clinical Text Analysis and Knowledge Extraction System (cTAKES) package was 0.69. Localization and keyword grouping improved system accuracy to 0.9 and 0.92, respectively. F-measures for current and past smoker classes improved from 0.43 to 0.81 and 0.71 to 0.91, respectively. Non-smoker and unknown-class F-measures were 0.96 and 0.98, respectively. Keyword grouping had no negative effect on performance, and decreased training time. Grouping keywords is a practical method to reduce training corpus size.

关键词： classification/methods Medical Records Systems, Computerized Natural Language Processing Smoking

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tqDist: a library for computing the quartet and triplet distances between binary or general trees

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BIOINFORMATICS 2014年第14期30卷 2079-2080页

作者： Sand, Andreas Holt, Morten K. Johansen, Jens Brodal, Gerth Stolting Mailund, Thomas Pedersen, Christian N. S. Aarhus Univ Bioinformat Res Ctr DK-8000 Aarhus C Denmark Aarhus Univ Dept Comp Sci DK-8000 Aarhus C Denmark Aarhus Univ MADALGO Ctr Mass Data Algorithms Ctr Danish Natl Res Fdn DK-8000 Aarhus C Denmark

.Summary: tqDist is a software package for computing the triplet and quartet distances between general rooted or unrooted trees, respectively. The program is based on algorithms with running time O(n log n) for the triplet distance calculation and O(d . n log n) for the quartet distance calculation, where n is the number of leaves in the trees and d is the degree of the tree with minimum degree. These are currently the fastest algorithms both in theory and in practice.

关键词： Algorithms classification/methods Phylogeny Phylogeny Software

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STAMP: statistical analysis of taxonomic and functional profiles

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BIOINFORMATICS 2014年第21期30卷 3123-3124页

作者： Parks, Donovan H. Tyson, Gene W. Hugenholtz, Philip Beiko, Robert G. Univ Queensland Australian Ctr Ecogen Sch Chem & Mol Biosci St Lucia Qld Australia Univ Queensland Adv Water Management Ctr St Lucia Qld Australia Univ Queensland Inst Mol Biosci St Lucia Qld Australia Dalhousie Univ Halifax NS Canada

STAMP is a graphical software package that provides statistical hypothesis tests and exploratory plots for analysing taxonomic and functional profiles. It supports tests for comparing pairs of samples or samples organized into two or more treatment groups. Effect sizes and confidence intervals are provided to allow critical assessment of the biological relevancy of test results. A user-friendly graphical interface permits easy exploration of statistical results and generation of publication-quality plots.

关键词： Bacteria/classification classification/methods Confidence Intervals Cyanobacteria/classification Cyanobacteria/genetics Data Interpretation, Statistical Genome, Bacterial Genome, Bacterial Humans Software

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Specimen collection: An essential tool

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SCIENCE 2014年第6186期344卷 814-+页

作者： Rocha, L. A. Aleixo, A. Allen, G. Almeda, F. Baldwin, C. C. Barclay, M. V. L. Bates, J. M. Bauer, A. M. Benzoni, F. Berns, C. M. Berumen, M. L. Blackburn, D. C. Blum, S. Bolanos, F. Bowie, R. C. K. Britz, R. Brown, R. M. Cadena, C. D. Carpenter, K. Ceriaco, L. M. Chakrabarty, P. Chaves, G. Choat, J. H. Clements, K. D. Collette, B. B. Collins, A. Coyne, J. Cracraft, J. Daniel, T. de Carvalho, M. R. de Queiroz, K. Di Dario, F. Drewes, R. Dumbacher, J. P. Engilis, A., Jr. Erdmann, M. V. Eschmeyer, W. Feldman, C. R. Fisher, B. L. Fjeldsa, J. Fritsch, P. W. Fuchs, J. Getahun, A. Gill, A. Gomon, M. Gosliner, T. Graves, G. R. Griswold, C. E. Guralnick, R. Hartel, K. Helgen, K. M. Ho, H. Iskandar, D. T. Iwamoto, T. Jaafar, Z. James, H. F. Johnson, D. Kavanaugh, D. Knowlton, N. Lacey, E. Larson, H. K. Last, P. Leis, J. M. Lessios, H. Liebherr, J. Lowman, M. Mahler, D. L. Mamonekene, V. Matsuura, K. Mayer, G. C. Mays, H., Jr. McCosker, J. McDiarmid, R. W. McGuire, J. Miller, M. J. Mooi, R. Mooi, R. D. Moritz, C. Myers, P. Nachman, M. W. Nussbaum, R. A. Foighil, D. O. Parenti, L. R. Parham, J. F. Paul, E. Paulay, G. Perez-Eman, J. Perez-Matus, A. Poe, S. Pogonoski, J. Rabosky, D. L. Randall, J. E. Reimer, J. D. Robertson, D. R. Roedel, M. -O. Rodrigues, M. T. Roopnarine, P. Rueber, L. Ryan, M. J. Sheldon, F. Shinohara, G. Short, A. Simison, W. B. Smith-Vaniz, W. F. Springer, V. G. Stiassny, M. Tello, J. G. Thompson, C. W. Trnski, T. Tucker, P. Valqui, T. Vecchione, M. Verheyen, E. Wainwright, P. C. Wheeler, T. A. White, T. Will, K. Williams, J. T. Williams, G. Wilson, E. O. Winker, K. Winterbottom, R. Witt, C. C. Calif Acad Sci San Francisco CA 94118 USA Museu Paraense Emilio Goeldi BR-66040170 Belem Para Brazil Western Australian Museum Perth WA 6986 Australia Smithsonian Inst Washington DC 20560 USA Nat Hist Museum London SW7 5BD England Field Museum Nat Hist Chicago IL 60605 USA Villanova Univ Villanova PA 19085 USA Univ Milano Bicocca I-20126 Milan Italy Utica Coll Utica NY 13502 USA King Abdullah Univ Sci & Technol Thuwal 23955 Saudi Arabia Univ Costa Rica San Jose 115012060 Costa Rica Univ Calif Berkeley Berkeley CA 94720 USA Univ Kansas Lawrence KS 66045 USA Univ Los Andes Bogota 4976 Colombia Old Dominion Univ Norfolk VA 23529 USA Museu Nacl Hist Nat & Ciencia P-7005638 Lisbon Portugal Louisiana State Univ Baton Rouge LA 70803 USA James Cook Univ Townsville Qld 4811 Australia Univ Auckland Auckland 1142 New Zealand NOAA Systemat Lab Washington DC 20013 USA Univ Chicago Chicago IL 60637 USA Amer Museum Nat Hist New York NY 10024 USA Univ Sao Paulo BR-05508090 Sao Paulo Brazil Univ Fed Rio de Janeiro BR-27965045 Macae RJ Brazil Univ Calif Davis Davis CA 95616 USA Conservat Int Denpasar 80235 Bali Indonesia Univ Nevada Reno NV 89557 USA Nat Hist Museum Denmark DK-2100 Copenhagen Denmark Museum Natl Hist Nat F-75005 Paris France Univ Addis Ababa Addis Ababa 1176 Ethiopia Univ Sydney Sydney NSW 2006 Australia Museum Victoria Melbourne Vic 3001 Australia Univ Colorado Boulder CO 80309 USA Harvard Univ Cambridge MA 02138 USA Natl Museum Marine Biol & Aquarium Pingtung 944 Taiwan Inst Teknol Bandung Bandung 40132 Indonesia Natl Univ Singapore Singapore 117543 Singapore Museum & Art Gallery Northern Terr Darwin NT 0820 Australia CSIRO Marine & Atmospher Res Hobart Tas 7000 Australia Australian Museum Sydney NSW 2010 Australia Smithsonian Trop Res Inst Balboa 084303092 Panama Cornell Univ Ithaca NY 14853 USA Univ Marien Ngouabi Brazzaville Rep Congo Natl Museum Nat & Sci Tsukuba Ibaraki 3050005 Japan Univ Wisconsin Parkside K

A letter to the editor is presented in response to the article 'Avoiding (Re)extinction' by B. A. Minteer et al. from the April 18, 2014 issue regarding scientific specimen collection.

关键词： Animals Biology/methods classification/methods Endangered Species Endangered Species Extinction, Biological

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Platelet-Rich Plasma: The PAW classification System

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ARTHROSCOPY-THE JOURNAL OF ARTHROSCOPIC AND RELATED SURGERY 2012年第7期28卷 998-1009页

作者： DeLong, Jeffrey M. Russell, Ryan P. Mazzocca, Augustus D. Univ Connecticut Dept Orthopaed Surg Farmington CT 06034 USA Med Univ S Carolina Charleston SC 29425 USA

Platelet-rich plasma (PRP) has been the subject of hundreds of publications in recent years. Reports of its effects in tissue, both positive and negative, have generated great interest in the orthopaedic community. Protocols for PRP preparation vary widely between authors and are often not well documented in the literature, making results difficult to compare or replicate. A classification system is needed to more accurately compare protocols and results and effectively group studies together for meta-analysis. Although some classification systems have been proposed, no single system takes into account the multitude of variables that determine the efficacy of PRP. In this article we propose a simple method for organizing and comparing results in the literature. The PAW classification system is based on 3 components: (1) the absolute number of Platelets, (2) the manner in which platelet Activation occurs, and (3) the presence or absence of White cells. By analyzing these 3 variables, we are able to accurately compare publications.

关键词： Blood Buffy Coat classification/methods Guided Tissue Regeneration/methods Humans Leukocyte Count Leukocyte Count Leukocytes Orthopedic Procedures/methods Platelet Activation Platelet Count Platelet Count Platelet-Rich Plasma/chemistry Platelet-Rich Plasma/cytology Platelet-Rich Plasma/metabolism

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The name game

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NATURE 2012年第7415期489卷 178-178页

作者： [Anonymous]

After several years of wrangling, zoologists can now name new species online only.

关键词： Animals Botany/methods classification/methods Dinosaurs Internet Paleontology/methods Publishing/trends Species Specificity Zoology/methods

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Improving structure alignment-based prediction of SCOP families using Vorolign Kernels

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BIOINFORMATICS 2011年第2期27卷 204-210页

作者： Hamp, Tobias Birzele, Fabian Buchwald, Fabian Kramer, Stefan Tech Univ Munich Inst Informat 12 D-85749 Garching Germany Boehringer Ingelheim Pharma GmbH & Co KG Grp Genom Dept Pulm Res D-88397 Biberach Germany

Motivation: The slow growth of expert-curated databases compared to experimental databases makes it necessary to build upon highly accurate automated processing pipelines to make the most of the data until curation becomes available. We address this problem in the context of protein structures and their classification into structural and functional classes, more specifically, the structural classification of proteins (SCOP). Structural alignment methods like Vorolign already provide good classification results, but effectively work in a 1-Nearest Neighbor mode. Model-based (in contrast to instance-based) approaches so far have been shown to be of limited values due to small classes arising in such classification schemes. Results: In this article, we describe how kernels defined in terms of Vorolign scores can be used in SVM learning, and explore variants of combined instance-based and model-based learning, up to exclusively model-based learning. Our results suggest that kernels based on Vorolign scores are effective and that model-based learning can yield highly competitive classification results for the prediction of SCOP families.

关键词： Algorithms Artificial Intelligence classification/methods Models, Molecular Protein Structure, Tertiary Proteins/chemistry Proteins/classification

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Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds

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BIOINFORMATICS 2011年第11期27卷 1537-1545页

作者： Mu, Fangping Unkefer, Clifford J. Unkefer, Pat J. Hlavacek, William S. Los Alamos Natl Lab Div Theoret Theoret Biol & Biophys Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Biosci Div Los Alamos NM 87545 USA

Results: We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is approximate to 0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment.

关键词： Artificial Intelligence Biocatalysis classification/methods Computational Biology/methods Databases, Factual Enzymes/classification Metabolic Networks and Pathways Metabolome Metabolomics/methods Molecular Structure

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