Publications
36 results found
Ma A, Lu X, Gray C, et al., 2019, Ecological networks reveal resilience of agro-ecosystems to changes in farming management, NATURE ECOLOGY & EVOLUTION, Vol: 3, Pages: 260-+, ISSN: 2397-334X
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- Citations: 14
Bohan D, Dumbrell A, Raybould A, et al., 2017, Next-generation global biomonitoring: large-scale, automated reconstruction of ecological networks, Trends in Ecology and Evolution, Vol: 32, Pages: 477-487, ISSN: 1872-8383
We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth’s environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth’s major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change.
Cropper A, Tamaddoni-Nezhad A, Muggleton SH, 2016, Meta-interpretive learning of data transformation programs, 25th International Conference, ILP 2015, Publisher: Springer, Pages: 46-59, ISSN: 0302-9743
Data transformation involves the manual construction of large numbers of special-purpose programs. Although typically small, such programs can be complex, involving problem decomposition, recursion, and recognition of context. Building such programs is common in commercial and academic data analytic projects and can be labour intensive and expensive, making it a suitable candidate for machine learning. In this paper, we use the meta-interpretive learning framework (MIL) to learn recursive data transformation programs from small numbers of examples. MIL is well suited to this task because it supports problem decomposition through predicate invention, learning recursive programs, learning from few examples, and learning from only positive examples. We apply Metagol, a MIL implementation, to both semi-structured and unstructured data. We conduct experiments on three real-world datasets: medical patient records, XML mondial records, and natural language taken from ecological papers. The experimental results suggest that high levels of predictive accuracy can be achieved in these tasks from small numbers of training examples, especially when learning with recursion.
Gill RJ, Woodward G, 2016, Networking our way to better ecosystem service provision, Trends in Ecology & Evolution, Vol: 31, Pages: 105-115, ISSN: 0169-5347
The ecosystem services (EcoS) concept is being used increasingly to attach values to natural systems and the multiple benefits they provide to human societies. Ecosystem processes or functions only become EcoS if they are shown to have social and/or economic value. This should assure an explicit connection between the natural and social sciences, but EcoS approaches have been criticized for retaining little natural science. Preserving the natural, ecological science context within EcoS research is challenging because the multiple disciplines involved have very different traditions and vocabularies (common-language challenge) and span many organizational levels and temporal and spatial scales (scale challenge) that define the relevant interacting entities (interaction challenge). We propose a network-based approach to transcend these discipline challenges and place the natural science context at the heart of EcoS research.
Vacher C, Tamaddoni-Nezhad A, Kamenova S, et al., 2016, Learning Ecological Networks from Next-Generation Sequencing Data, ECOSYSTEM SERVICES: FROM BIODIVERSITY TO SOCIETY, PT 2, Editors: Woodward, Bohan, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 1-39, ISBN: 978-0-08-100978-9
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- Citations: 65
Pocock MJO, Evans DM, Fontaine C, et al., 2016, The Visualisation of Ecological Networks, and Their Use as a Tool for Engagement, Advocacy and Management, ECOSYSTEM SERVICES: FROM BIODIVERSITY TO SOCIETY, PT 2, Editors: Woodward, Bohan, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 41-85, ISBN: 978-0-08-100978-9
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- Citations: 43
Muggleton SH, Lin D, Tamaddoni Nezhad A, 2015, Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited, Machine Learning, Vol: 100, Pages: 49-73, ISSN: 1573-0565
Since the late 1990s predicate invention has been under-explored within inductivelogic programming due to difficulties in formulating efficient search mechanisms. However,a recent paper demonstrated that both predicate invention and the learning of recursion canbe efficiently implemented for regular and context-free grammars, by way of metalogicalsubstitutions with respect to a modified Prolog meta-interpreter which acts as the learningengine. New predicate symbols are introduced as constants representing existentiallyquantified higher-order variables. The approach demonstrates that predicate invention can betreated as a form of higher-order logical reasoning. In this paper we generalise the approachof meta-interpretive learning (MIL) to that of learning higher-order dyadic datalog programs.We show that with an infinite signature the higher-order dyadic datalog class H22 has universalTuring expressivity though H22 is decidable given a finite signature. Additionally weshow that Knuth–Bendix ordering of the hypothesis space together with logarithmic clausebounding allows our MIL implementation MetagolD to PAC-learn minimal cardinality H22definitions. This result is consistent with our experiments which indicate that MetagolDefficiently learns compact H22 definitions involving predicate invention for learning roboticstrategies, the East–West train challenge and NELL. Additionally higher-order concepts werelearned in the NELL language learning domain. The Metagol code and datasets described inthis paper have been made publicly available on a website to allow reproduction of results inthis paper.
Tamaddoni-Nezhad A, Bohan D, Raybould A, et al., 2015, Towards Machine Learning of Predictive Models from Ecological Data, 24th International Conference on Inductive Logic Programming (ILP), Publisher: SPRINGER-VERLAG BERLIN, Pages: 154-167, ISSN: 0302-9743
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- Citations: 4
Muggleton SH, Lin D, Pahlavi N, et al., 2014, Meta-interpretive learning: application to grammatical inference, MACHINE LEARNING, Vol: 94, Pages: 25-49, ISSN: 0885-6125
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- Citations: 76
Muggleton SH, Lin D, Chen J, et al., 2014, MetaBayes: Bayesian Meta-Interpretative Learning using Higher-Order Stochastic Refinement
Tamaddoni-Nezhad A, Milani GA, Raybould A, et al., 2013, Construction and Validation of Food-webs using Logic-based Machine Learning and Text-mining, Pages: 225-289
Bohan DA, Raybould A, Mulder C, et al., 2013, Networking Agroecology: Integrating the Diversity of Agroecosystem Interactions, ADVANCES IN ECOLOGICAL RESEARCH, VOL 49: ECOLOGICAL NETWORKS IN AN AGRICULTURAL WORLD, Editors: Woodward, Bohan, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 1-67, ISBN: 978-0-12-420002-9
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- Citations: 53
Sternberg MJE, Tamaddoni-Nezhad A, Lesk VI, et al., 2012, Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach, Journal of Molecular Biology, Vol: 425, Pages: 186-197, ISSN: 1089-8638
Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system.
Bohen DA, Caron-Lormier G, Muggleton SH, et al., 2011, Automated Discovery of Food Webs from Ecological Data using Logic-based Machine Learning, PloS ONE, Vol: 6
Tamaddoni-Nezhad A, Muggleton S, 2011, Stochastic Refinement, 20th International Conference on Inductive Logic Programming (ILP), Publisher: SPRINGER-VERLAG BERLIN, Pages: 222-237, ISSN: 0302-9743
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- Citations: 2
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
Muggleton S, Santos J, Tamaddoni-Nezhad A, 2010, ProGolem: A System Based on Relative Minimal Generalisation, 19th International Conference on Inductive Logic Programming, Publisher: SPRINGER-VERLAG BERLIN, Pages: 131-148, ISSN: 0302-9743
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- Citations: 12
Tamaddoni-Nezhad A, Muggleton S, 2009, The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause, MACHINE LEARNING, Vol: 76, Pages: 37-72, ISSN: 0885-6125
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- Citations: 13
Santos JCA, Tamaddoni-Nezhad A, Muggleton S, 2009, An ILP System for Learning Head Output Connected Predicates, 14th Portuguese Conference on Artificial Intelligence, Publisher: SPRINGER-VERLAG BERLIN, Pages: 150-159, ISSN: 0302-9743
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- Citations: 3
Muggleton S, Tamaddoni-Nezhad A, 2008, QG/GA: a stochastic search for Progol, 16th International Conference of Inductive Logic Programming, Publisher: SPRINGER, Pages: 121-133, ISSN: 0885-6125
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- Citations: 18
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, Santos JCA, Tamaddoni-Nezhad A, 2008, TopLog: ILP Using a Logic Program Declarative Bias, 24th International Conference on Logic Programming (ICLP), Publisher: SPRINGER-VERLAG BERLIN, Pages: 687-692, ISSN: 0302-9743
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- Citations: 18
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
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
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
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
Tamaddoni-Nezhad A, Chaleil R, Kakas A, et al., 2005, Abduction and induction for learning models of inhibition in metabolic networks, Los Alamitos, 4th international conference on machine learning and applications, 15 - 17 December 2005, Los Angeles, CA, Publisher: Ieee Computer Soc, Pages: 233-238
Tamaddoni-Nezhad A, Kakas A, Muggleton S, et al., 2004, Modelling inhibition in metabolic pathways through abduction and induction, Berlin, 14th international conference on inductive logic programming (ILP 2004), Porto, Portugal, Publisher: Springer-Verlag, Pages: 305-322
Tamaddoni-Nezhad A, Muggleton S, 2003, A genetic algorithms approach to ILP, Berlin, 12th international conference on inductive logic programming, Sydney, Australia, 2002, Publisher: Springer-Verlag, Pages: 285-300
Muggleton S, Tamaddoni-Nezhad A, Watanabe H, 2003, Induction of enzyme classes from biological databases, Berlin, 13th international conference on inductive logic programming, Szeged, Hungary, Publisher: Springer-Verlag, Pages: 269-280
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