Imperial College London

ProfessorMichaelSternberg

Faculty of Natural SciencesDepartment of Life Sciences

Director Centre for Bioinformatics
 
 
 
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Contact

 

+44 (0)20 7594 5212m.sternberg Website

 
 
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Location

 

306Sir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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321 results found

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

JOURNAL ARTICLE

Sinden RE, Talman A, Marques SR, Wass MN, Sternberg MJEet al., 2010, The flagellum in malarial parasites, CURRENT OPINION IN MICROBIOLOGY, Vol: 13, Pages: 491-500, ISSN: 1369-5274

JOURNAL ARTICLE

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

JOURNAL ARTICLE

Chambers JC, Zhang W, Li Y, Sehmi J, Wass MN, Zabaneh D, Hoggart C, Bayele H, McCarthy MI, Peltonen L, Freimer NB, Srai SK, Maxwell PH, Sternberg MJE, Ruokonen A, Abecasis G, Jarvelin M-R, Scott J, Elliott P, Kooner JSet al., 2009, Genome-wide association study identifies variants in TMPRSS6 associated with hemoglobin levels, NATURE GENETICS, Vol: 41, Pages: 1170-1172, ISSN: 1061-4036

JOURNAL ARTICLE

Hermoso A, Espadaler J, Enrique Querol E, Aviles FX, Sternberg MJE, Oliva B, Fernandez-Fuentes Net 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

JOURNAL ARTICLE

Kelley LA, Shrimpton PJ, Muggleton SH, Sternberg MJEet 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

JOURNAL ARTICLE

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

JOURNAL ARTICLE

Laskowski RA, Thornton JM, Sternberg MJE, 2009, The fine details of evolution, BIOCHEMICAL SOCIETY TRANSACTIONS, Vol: 37, Pages: 723-726, ISSN: 0300-5127

JOURNAL ARTICLE

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

CONFERENCE PAPER

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.

CONFERENCE PAPER

Sternberg M, Thornton J, Laskowski R, 2009, Protein evolution - Sequence, structure and systems, Pages: 52-52, ISSN: 0954-982X

CONFERENCE PAPER

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

JOURNAL ARTICLE

Bang J-W, Crockford DJ, Hohmes E, Pazos F, Sternberg MJE, Muggleton SH, Nicholson JKet 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

JOURNAL ARTICLE

Bang J-W, Crockford DJ, Holmes E, Pazos F, Sternberg MJE, Muggleton SH, Nicholson JKet 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

JOURNAL ARTICLE

Bang J-W, Crockford DJ, Holmes E, Pazos F, Sternberg MJE, Muggleton SH, Nicholson JKet al., 2008, Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods., J Proteome Res, Vol: 7, Pages: 497-503, ISSN: 1535-3893

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.

JOURNAL ARTICLE

Barton G, Abbott J, Chiba N, Huang DW, Huang Y, Krznaric M, Mack-Smith J, Saleem A, Sherman BT, Tiwari B, Tomlinson C, Aitman T, Darlington J, Game L, Sternberg MJE, Butcher SAet al., 2008, EMAAS: An extensible grid-based Rich Internet Application for microarray data analysis and management, BMC BIOINFORMATICS, Vol: 9, ISSN: 1471-2105

JOURNAL ARTICLE

Bennett-Lovsey RM, Herbert AD, Sternberg MJE, Kelley LAet 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

JOURNAL ARTICLE

Chen J, Kelley L, Muggleton S, Sternberg Met 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.

CONFERENCE PAPER

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

JOURNAL ARTICLE

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

JOURNAL ARTICLE

Tsunoyama K, Amini A, Sternberg MJE, Muggleton SHet 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

JOURNAL ARTICLE

Wass MN, Sternberg MJE, 2008, ConFunc - functional annotation in the twilight zone, BIOINFORMATICS, Vol: 24, Pages: 798-806, ISSN: 1367-4803

JOURNAL ARTICLE

Amini A, Muggleton SH, Lodhi H, Sternberg MJEet al., 2007, A novel logic-based approach for quantitative toxicology prediction, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 47, Pages: 998-1006, ISSN: 1549-9596

JOURNAL ARTICLE

Amini A, Shrimpton PJ, Muggleton SH, Sternberg MJEet al., 2007, A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 69, Pages: 823-831, ISSN: 0887-3585

JOURNAL ARTICLE

Cannon EO, Amini A, Bender A, Sternberg MJE, Muggleton SH, Glen RC, Mitchell JBOet al., 2007, Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds, JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, Vol: 21, Pages: 269-280, ISSN: 0920-654X

JOURNAL ARTICLE

Chen J, Kelley L, Muggleton S, Sternberg Met al., 2007, Multi-class prediction using stochastic logic programs, 16th International Conference on Inductive Logic Programming, Publisher: SPRINGER-VERLAG BERLIN, Pages: 109-+, ISSN: 0302-9743

CONFERENCE PAPER

Cootes AP, Muggleton SH, Sternberg MJE, 2007, The identification of similarities between biological networks: Application to the metabolome and interactome, JOURNAL OF MOLECULAR BIOLOGY, Vol: 369, Pages: 1126-1139, ISSN: 0022-2836

JOURNAL ARTICLE

Gherardini PF, Wass MN, Helmer-Citterich M, Sternberg MJEet al., 2007, Convergent evolution of enzyme active sites is not a rare phenomenon, JOURNAL OF MOLECULAR BIOLOGY, Vol: 372, Pages: 817-845, ISSN: 0022-2836

JOURNAL ARTICLE

Tamaddoni-Nezhad A, Chaleil R, Kakas AC, Sternberg M, Nicholson J, Muggleton Set al., 2007, Modeling the effects of toxins in metabolic networks - Abductive and inductive reasoning for learning models of inhibition in biological networks, IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, Vol: 26, Pages: 37-46, ISSN: 0739-5175

JOURNAL ARTICLE

Fleming K, Kelley LA, Islam SA, MacCallum RM, Muller A, Pazos F, Sternberg MJEet al., 2006, The proteome: structure, function and evolution, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 361, Pages: 441-451, ISSN: 0962-8436

JOURNAL ARTICLE

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