Alireza Tamaddoni-Nezhad
TitleCited byYear
Inductive logic programming
S Muggleton, R Otero, A Tamaddoni-Nezhad
Academic Press, 2006
1010*2006
Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited
SH Muggleton, D Lin, A Tamaddoni-Nezhad
Machine Learning 100 (1), 49-73, 2015
1302015
Application of abductive ILP to learning metabolic network inhibition from temporal data
A Tamaddoni-Nezhad, R Chaleil, A Kakas, S Muggleton
Machine Learning 64 (1-3), 209-230, 2006
952006
Meta-interpretive learning: application to grammatical inference
SH Muggleton, D Lin, N Pahlavi, A Tamaddoni-Nezhad
Machine learning 94 (1), 25-49, 2014
922014
Networking agroecology: integrating the diversity of agroecosystem interactions
DA Bohan, A Raybould, C Mulder, G Woodward, A Tamaddoni-Nezhad, ...
Advances in Ecological Research 49, 1-67, 2013
572013
Automated discovery of food webs from ecological data using logic-based machine learning
DA Bohan, G Caron-Lormier, S Muggleton, A Raybould, ...
PLoS One 6 (12), e29028, 2011
492011
Next-generation global biomonitoring: large-scale, automated reconstruction of ecological networks
DA Bohan, C Vacher, A Tamaddoni-Nezhad, A Raybould, AJ Dumbrell, ...
Trends in Ecology & Evolution 32 (7), 477-487, 2017
462017
ProGolem: a system based on relative minimal generalisation
S Muggleton, J Santos, A Tamaddoni-Nezhad
International Conference on Inductive Logic Programming, 131-148, 2009
462009
Learning ecological networks from next-generation sequencing data
C Vacher, A Tamaddoni-Nezhad, S Kamenova, N Peyrard, Y Moalic, ...
Advances in Ecological Research 54, 1-39, 2016
402016
Construction and validation of food webs using logic-based machine learning and text mining
A Tamaddoni-Nezhad, GA Milani, A Raybould, S Muggleton, DA Bohan
Advances in Ecological Research 49, 225-289, 2013
402013
TopLog: ILP using a logic program declarative bias
SH Muggleton, JCA Santos, A Tamaddoni-Nezhad
International Conference on Logic Programming, 687-692, 2008
372008
QG/GA: a stochastic search for Progol
S Muggleton, A Tamaddoni-Nezhad
Machine Learning 70 (2-3), 121-133, 2008
342008
Searching the subsumption lattice by a genetic algorithm
A Tamaddoni-Nezhad, SH Muggleton
International Conference on Inductive Logic Programming, 243-252, 2000
332000
A genetic algorithms approach to ILP
A Tamaddoni-Nezhad, S Muggleton
International Conference on Inductive Logic Programming, 285-300, 2002
292002
The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause
A Tamaddoni-Nezhad, S Muggleton
Machine Learning 76 (1), 37-72, 2009
272009
Modelling inhibition in metabolic pathways through abduction and induction
A Tamaddoni-Nezhad, A Kakas, S Muggleton, F Pazos
International Conference on Inductive Logic Programming, 305-322, 2004
252004
The visualisation of ecological networks, and their use as a tool for engagement, advocacy and management
MJO Pocock, DM Evans, C Fontaine, M Harvey, R Julliard, Ó McLaughlin, ...
Advances in Ecological Research 54, 41-85, 2016
232016
How does predicate invention affect human comprehensibility?
U Schmid, C Zeller, T Besold, A Tamaddoni-Nezhad, S Muggleton
International Conference on Inductive Logic Programming, 52-67, 2016
212016
Gene function hypotheses for the Campylobacter jejuni glycome generated by a logic-based approach
MJE Sternberg, A Tamaddoni-Nezhad, VI Lesk, E Kay, PG Hitchen, ...
Journal of Molecular Biology, 2013
202013
Metabayes: Bayesian meta-interpretative learning using higher-order stochastic refinement
SH Muggleton, D Lin, J Chen, A Tamaddoni-Nezhad
International Conference on Inductive Logic Programming, 1-17, 2013
172013
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Articles 1–20