Publications
281 results found
Tamaddoni-Nezhad A, Muggleton S, 2008, A Note on Refinement Operators for IE-Based ILP Systems, 18th International Conference on Inductive Logic Programming, Publisher: SPRINGER-VERLAG BERLIN, Pages: 297-314, ISSN: 0302-9743
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- Citations: 2
Muggleton SH, 2008, Developing Robust Synthetic Biology Designs Using a Microfluidic Robot Scientist., Publisher: Springer, Pages: 4-4
Fidjeland A, Luk W, Muggleton SH, 2008, A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming., Publisher: British Computer Society, Pages: 318-330
, 2008, Probabilistic Inductive Logic Programming - Theory and Applications, Publisher: Springer
, 2008, Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007, Publisher: Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany
Muggleton S, Otero R, Tamaddoni-Nezhad A, 2007, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, ISBN: 9783540738466
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
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- Citations: 27
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
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- Citations: 40
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
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- Citations: 33
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
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- Citations: 9
Raedt LD, Dietterich TG, Getoor L, et al., 2007, 07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis., Publisher: Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany
Natarajan S, Tadepalli P, Fern A, 2007, Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies., Publisher: Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany
Chen J, Muggleton SH, Santos J, 2007, Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning.
Muggleton S, Tamaddoni-Nezhad A, 2007, QG/GA: A stochastic search for progol, 16th International Conference on Inductive Logic Programming, Publisher: SPRINGER-VERLAG BERLIN, Pages: 37-+, 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.
Muggleton S, Pahlavi N, 2007, The complexity of translating BLPs to RMMs, 16th International Conference on Inductive Logic Programming, Publisher: SPRINGER-VERLAG BERLIN, Pages: 351-+, ISSN: 0302-9743
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
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- Citations: 1
, 2007, Inductive Logic Programming, 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers, Publisher: Springer
Muggleton SH, Lodhi H, Amini A, et al., 2006, Support vector inductive logic programming, Studies in Fuzziness and Soft Computing, Vol: 194, Pages: 113-135, ISSN: 1434-9922
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.
Kakas A, Tamaddoni Nezhad A, Muggleton S, et al., 2006, Application of abductive ILP to learning metabolic network inhibition from temporal data, Publisher: Springer, Pages: 209-230, ISSN: 0885-6125
In this paper we use a logic-based representation and a combination of Abduction and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning\r\nof general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances designed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon\r\nof inhibition in metabolic networks, background knowledge is used which describes the network topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from\r\nin vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxins. The modelÆs performance is evaluated on training and test sets randomly generated from a real metabolic network. It is shown that even in\r\nthe case where the hypotheses are restricted to be ground, the predictive accuracy increases with the number of training examples and in all cases exceeds the default (majority class).\r\nExperimental results also suggest that when sufficient training data is provided
Colton S, Muggleton S, 2006, Mathematical applications of inductive logic programming, Machine Learning, Vol: 64, Pages: 25-64, ISSN: 0885-6125
Colton S, Muggleton S, 2006, Mathematical applications of inductive logic programming, Machine Learning
Muggleton SH, 2006, Exceeding human limits, NATURE, Vol: 440, Pages: 409-410, ISSN: 0028-0836
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- Citations: 31
De Raedt L, Dietterich T, Getoor L, et al., 2006, Probabilistic, Logical and Relational Learning Toward a Synthesis Dagstuhl Seminar 05051, January 30 - February 4, 2005 Executive Summary, ISSN: 1862-4405
Tamaddoni-Nezhad T, Chaleil R, Kakas A, et al., 2006, Application of adbuctive ILP to learning metabolic network inhibition from temporal data, Machine Learning, Vol: 64, Pages: 209-230, ISSN: 0885-6125
, 2006, Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005, Publisher: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany
Chaleil R, Kakas A, Muggleton S, et al., 2005, Abduction and induction for modelling inhibition in metabolic networks, 4th International Conference on Machine Learning and Applications, Publisher: IEEE Computer Society
Muggleton S, 2005, Towards Chemical Universal Turing Machines, 21st National Conference on Artificial Intelligence
Muggleton S, Watanabe H, 2005, Learning Stochastic Logical Automaton, 19th Annual Conferences of JSAI, Publisher: Springer Verlag
Muggleton S, 2005, Machine Learning for Systems Biology, 15th International Conference on Inductive Logic Programming, Publisher: Springer-Verlag
In this paper we survey work being conducted at Imperial College\r\non the use of machine learning to build Systems Biology models of the effects\r\nof toxins on biochemical pathways. Several distinct, and complementary modelling\r\ntechniques are being explored. Firstly, work is being conducted on applying\r\nSupport-Vector ILP (SVILP) as an accurate means of screening high-toxicity\r\nmolecules. Secondly, Bayes' networks have been machine-learned to provide\r\ncausal maps of the effects of toxins on the network of metabolic reactions within\r\ncells. The data were derived from a study on the effects of hydrazine toxicity in\r\nrats. Although the resultant network can be partly explained in terms of existing\r\nKEGG (Kyoto Encyclopedia of Genes and Genomes) pathway descriptions, several\r\nof the strong dependencies in the Bayes' network involve metabolite pairs\r\nwith high separation in KEGG. Thirdly, in a complementary study KEGG pathways\r\nare being used as background knowledge for explaining the same data using\r\na model constructed using Abductive ILP, a logic-based machine learning\r\ntechnique. With a binary prediction model (up/down regulation) cross validation\r\nresults show that even with a restricted number of observed metabolites high\r\npredictive accuracy (80-90%) is achieved on unseen metabolite concentrations.\r\nFurther increases in accuracy are achieved by allowing discovery of general rules\r\nfrom additional literature data on hydrazine inhibition. Ongoing work is aimed\r\nat formulating probabilistic logic models which combine the learned Bayes' network\r\nand ILP models.
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