342 results found
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
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
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
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
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
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., 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.
Amini A, Shrimpton PJ, Muggleton SH, et 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
Gherardini PF, Wass MN, Helmer-Citterich M, et 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
Amini A, Muggleton SH, Lodhi H, et 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
Cannon EO, Amini A, Bender A, et 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
Tamaddoni-Nezhad A, Chaleil R, Kakas AC, et 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
Chen J, Kelley L, Muggleton S, et 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
Cootes AP, Muggleton SH, Sternberg MJE, 2007, The identification of similarities between biological networks: Application to the metabolome and interactome prediction, Molecular Biology, Vol: 369, Pages: 1126-1139
The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to life, such as purine, pyrimidine and amino acid metabolism. The similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.
In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP). We propose a general method for constructing kernels for Support Vector Inductive Logic Programming (SVILP). The kernel not only captures the semantic and syntactic relational information contained in the data but also provides the flexibility of using arbitrary forms of structured and non-structured data coded in a relational way. While specialised kernels have been developed for strings, trees and graphs our approach uses declarative background knowledge to provide the learning bias. The use of explicitly encoded background knowledge distinguishes SVILP from existing relational kernels which in ILP-terms work purely at the atomic generalisation level. The SVILP approach is a form of generalisation relative to background knowledge, though the final combining function for the ILP-learned clauses is an SVM rather than a logical conjunction. We evaluate SVILP empirically against related approaches, including an industry-standard toxin predictor called TOPKAT. Evaluation is conducted on a new broad-ranging toxicity dataset (DSSTox). The experimental results demonstrate that our approach significantly outperforms all other approaches in the study. © Springer-Verlag Berlin Heidelberg 2006.
Paszkiewicz KH, Sternberg MJE, Lappe M, 2006, Prediction of viable circular permutants using a graph theoretic approach, BIOINFORMATICS, Vol: 22, Pages: 1353-1358, ISSN: 1367-4803
Jefferys BR, Kelley LA, Sergot MJ, et al., 2006, Capturing expert knowledge with argumentation: a case study in bioinformatics, BIOINFORMATICS, Vol: 22, Pages: 924-933, ISSN: 1367-4803
Jones DT, Sternberg MJE, Thornton JM, 2006, Introduction. Bioinformatics: from molecules to systems, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 361, Pages: 389-391, ISSN: 0962-8436
Fleming K, Kelley LA, Islam SA, et 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
Fernandez-Fuentes N, Querol E, Aviles FX, et al., 2005, Prediction of the conformation and geometry of loops in globular proteins: Testing ArchDB, a structural classification of loops, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 60, Pages: 746-757, ISSN: 0887-3585
Carter P, Lesk VI, Islam SA, et al., 2005, Protein-protein docking using 3D-dock in rounds 3, 4, and 5 of CAPRI, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 60, Pages: 281-288, ISSN: 0887-3585
Smith GR, Sternberg MJE, Bates PA, 2005, The relationship between the flexibility of proteins and their conformational states on forming protein-protein complexes with an application to protein-protein docking, JOURNAL OF MOLECULAR BIOLOGY, Vol: 347, Pages: 1077-1101, ISSN: 0022-2836
Madhusudan S, Smart F, Shrimpton P, et al., 2005, Isolation of a small molecule inhibitor of DNA base excision repair, NUCLEIC ACIDS RESEARCH, Vol: 33, Pages: 4711-4724, ISSN: 0305-1048
Pazos F, Ranea JAG, Juan D, et al., 2005, Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome, J MOL BIOL, Vol: 352, Pages: 1002-1015, ISSN: 0022-2836
Muggleton S, Lodhi H, Amini A, et al., 2005, Support vector inductive logic programming, Berlin, 8th International Conference on Discovery Science, 8 - 11 October 2005, Singapore, SINGAPORE, Publisher: Springer-Verlag, Pages: 163-175
Pazos F, Sternberg MJE, 2004, Automated prediction of protein function and detection of functional sites from structure, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 101, Pages: 14754-14759, ISSN: 0027-8424
Aguilar D, Aviles FX, Querol E, et al., 2004, Analysis of phenetic trees based on metabolic capabilites across the three domains of life, JOURNAL OF MOLECULAR BIOLOGY, Vol: 340, Pages: 491-512, ISSN: 0022-2836
Espadaler J, Fernandez-Fuentes N, Hermoso A, et al., 2004, ArchDB: automated protein loop classification as a tool for structural genomics, NUCLEIC ACIDS RESEARCH, Vol: 32, Pages: D185-D188, ISSN: 0305-1048
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