Publications
368 results found
, 2010, Proceedings of the 4th Meeting on the Critical Assessment of Predicted Interaction (CAPRI), Barcelona, Spain., Proteins, Vol: 78, Pages: 3065-3249
Fernandez-Recio J, Sternberg MJE, 2010, The 4th meeting on the Critical Assessment of PRedicted Interaction (CAPRI) held at the Mare Nostrum, Barcelona, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 78, Pages: 3065-3066, ISSN: 0887-3585
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- Citations: 14
Chubb D, Jefferys BR, Sternberg MJE, et al., 2010, Sequencing delivers diminishing returns for homology detection: implications for mapping the protein universe, BIOINFORMATICS, Vol: 26, Pages: 2664-2671, ISSN: 1367-4803
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- Citations: 17
Kay E, Lesk VI, Tamaddoni-Nezhad A, et al., 2010, Systems analysis of bacterial glycomes, BIOCHEMICAL SOCIETY TRANSACTIONS, Vol: 38, Pages: 1290-1293, ISSN: 0300-5127
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- Citations: 10
Lodhi H, Muggleton S, Sternberg MJE, 2010, Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods, MOLECULAR INFORMATICS, Vol: 29, Pages: 655-664, ISSN: 1868-1743
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- Citations: 1
Sinden RE, Talman A, Marques SR, et al., 2010, The flagellum in malarial parasites, CURRENT OPINION IN MICROBIOLOGY, Vol: 13, Pages: 491-500, ISSN: 1369-5274
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- Citations: 55
Wass MN, Kelley LA, Sternberg MJE, 2010, 3DLigandSite: predicting ligand-binding sites using similar structures, NUCLEIC ACIDS RESEARCH, Vol: 38, Pages: W469-W473, ISSN: 0305-1048
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- Citations: 472
Chambers JC, Zhang W, Lord GM, et al., 2010, Genetic loci influencing kidney function and chronic kidney disease, NATURE GENETICS, Vol: 42, Pages: 373-375, ISSN: 1061-4036
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- Citations: 215
Jefferys BR, Kelley LA, Sternberg MJE, 2010, Protein Folding Requires Crowd Control in a Simulated Cell, JOURNAL OF MOLECULAR BIOLOGY, Vol: 397, Pages: 1329-1338, ISSN: 0022-2836
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- Citations: 67
Chambers JC, Zhao J, Terracciano CMN, et al., 2010, Genetic variation in <i>SCN10A</i> influences cardiac conduction, NATURE GENETICS, Vol: 42, Pages: 149-U80, ISSN: 1061-4036
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- Citations: 204
Hermoso A, Espadaler J, Enrique Querol E, et al., 2009, Including Functional Annotations and Extending the Collection of Structural Classifications of Protein Loops (ArchDB)., Bioinform Biol Insights, Vol: 1, Pages: 77-90
Loops represent an important part of protein structures. The study of loop is critical for two main reasons: First, loops are often involved in protein function, stability and folding. Second, despite improvements in experimental and computational structure prediction methods, modeling the conformation of loops remains problematic. Here, we present a structural classification of loops, ArchDB, a mine of information with application in both mentioned fields: loop structure prediction and function prediction. ArchDB (http://sbi.imim.es/archdb) is a database of classified protein loop motifs. The current database provides four different classification sets tailored for different purposes. ArchDB-40, a loop classification derived from SCOP40, well suited for modeling common loop motifs. Since features relevant to loop structure or function can be more easily determined on well-populated clusters, we have developed ArchDB-95, a loop classification derived from SCOP95. This new classification set shows a ~40% increase in the number of subclasses, and a large 7-fold increase in the number of putative structure/function-related subclasses. We also present ArchDB-EC, a classification of loop motifs from enzymes, and ArchDB-KI, a manually annotated classification of loop motifs from kinases. Information about ligand contacts and PDB sites has been included in all classification sets. Improvements in our classification scheme are described, as well as several new database features, such as the ability to query by conserved annotations, sequence similarity, or uploading 3D coordinates of a protein. The lengths of classified loops range between 0 and 36 residues long. ArchDB offers an exhaustive sampling of loop structures. Functional information about loops and links with related biological databases are also provided. All this information and the possibility to browse/query the database through a web-server outline an useful tool with application in the comparative study of lo
Lodhi H, Muggleton S, Sternberg MJE, 2009, Multi-class protein fold recognition using large margin logic based divide and conquer learning, Pages: 22-26
Inductive Logic Programming (ILP) systems have been successfully applied to solve complex biological problem by viewing them as binary classification tasks. It remains an open question how an accurate solution to a multi-class problem can be obtained by using a logic based learning method. In this paper we present a novel logic based approach to solve complex and challenging multi-class classification problems in bioinformatics by focusing on a particular task, namely protein fold recognition. Our technique is based on the use of large margin kernel-based methods in conjunction with first order rules induced by an ILP system. The proposed approach learns a multi-class classifier by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. The method is applied to assigning protein domains to folds. Experimental evaluation of the method demonstrates the efficacy of the proposed approach to solving complex multi-class classification problems in bioinformatics. © 2009 ACM.
Chambers J, Zhao J, Terracciano C, et al., 2009, Genetic Variation in SCN10a is Associated With Cardiac Conduction, Heart Block and Risk of Ventricular Fibrillation, 82nd Scientific Session of the American-Heart-Association, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: S579-S580, ISSN: 0009-7322
Chambers JC, Zhang W, Li Y, et al., 2009, Genome-wide association study identifies variants in <i>TMPRSS6</i> associated with hemoglobin levels, NATURE GENETICS, Vol: 41, Pages: 1170-1172, ISSN: 1061-4036
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- Citations: 188
Kelley LA, Shrimpton PJ, Muggleton SH, et al., 2009, Discovering rules for protein-ligand specificity using support vector inductive logic programming, PROTEIN ENGINEERING DESIGN & SELECTION, Vol: 22, Pages: 561-567, ISSN: 1741-0126
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- Citations: 7
Laskowski RA, Thornton JM, Sternberg MJE, 2009, The fine details of evolution, BIOCHEMICAL SOCIETY TRANSACTIONS, Vol: 37, Pages: 723-726, ISSN: 0300-5127
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- Citations: 3
Sternberg M, Thornton J, Laskowski R, 2009, Protein evolution - Sequence, structure and systems, Pages: 52-52, ISSN: 0954-982X
Sternberg M, Ali SN, Helmer-Citterich M, et al., 2009, Evolution of protein structure and function, Annual Meeting of the Society-for-Experimental-Biology, Publisher: ELSEVIER SCIENCE INC, Pages: S47-S47, ISSN: 1095-6433
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- Citations: 1
Wass MN, Sternberg MJE, 2009, Prediction of ligand binding sites using homologous structures and conservation at CASP8, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 77, Pages: 147-151, ISSN: 0887-3585
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- Citations: 35
Lodhi H, Muggleton S, Sternberg MJE, 2009, Learning Large Margin First Order Decision Lists for Multi-Class Classification, 12th International Conference on Discovery Science, Publisher: SPRINGER-VERLAG BERLIN, Pages: 168-+, ISSN: 0302-9743
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- Citations: 3
Kelley LA, Sternberg MJE, 2009, Protein structure prediction on the Web: a case study using the Phyre server, NATURE PROTOCOLS, Vol: 4, Pages: 363-371, ISSN: 1754-2189
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- Citations: 3465
Barton G, Abbott J, Chiba N, et al., 2008, EMAAS: An extensible grid-based Rich Internet Application for microarray data analysis and management, BMC Bioinformatics, Vol: 9, ISSN: 1471-2105
BackgroundMicroarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management.ResultsEMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms.ConclusionEMAAS enables users to track and perform microarray data management and analysis tasks through
Dobbins SE, Lesk VI, Sternberg MJE, 2008, Insights into protein flexibility: The relationship between normal modes and conformational change upon protein-protein docking, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 105, Pages: 10390-10395, ISSN: 0027-8424
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- Citations: 179
Lesk VI, Sternberg MJE, 2008, 3D-Garden: a system for modelling proteinprotein complexes based on conformational refinement of ensembles generated with the marching cubes algorithm, BIOINFORMATICS, Vol: 24, Pages: 1137-1144, ISSN: 1367-4803
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- Citations: 43
Tsunoyama K, Amini A, Sternberg MJE, et al., 2008, Scaffold hopping in drug discovery using inductive logic programming, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 48, Pages: 949-957, ISSN: 1549-9596
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- Citations: 30
Chen J, Kelley L, Muggleton S, et al., 2008, Protein fold discovery using stochastic logic programs, Pages: 244-262, ISSN: 0302-9743
This chapter starts with a general introduction to protein folding. We then present a probabilistic method of dealing with multi-class classification, in particular multi-class protein fold prediction, using Stochastic Logic Programs (SLPs). Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class (protein fold) prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability. © 2008 Springer-Verlag Berlin Heidelberg.
Bang J-W, Crockford DJ, Holmes E, et al., 2008, Integrative top-down system metabolic modeling in experimental disease states via data-driven bayesian methods (vol 7, pg 497, 2008), JOURNAL OF PROTEOME RESEARCH, Vol: 7, Pages: 1352-1352, ISSN: 1535-3893
Wass MN, Sternberg MJE, 2008, ConFunc - functional annotation in the twilight zone, BIOINFORMATICS, Vol: 24, Pages: 798-806, ISSN: 1367-4803
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- Citations: 83
Bennett-Lovsey RM, Herbert AD, Sternberg MJE, et al., 2008, Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 70, Pages: 611-625, ISSN: 0887-3585
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- Citations: 344
Bang J-W, Crockford DJ, Hohmes E, et al., 2008, Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods, JOURNAL OF PROTEOME RESEARCH, Vol: 7, Pages: 497-503, ISSN: 1535-3893
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- Citations: 15
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