Jaume Bacardit
Jaume Bacardit
Professor of Artificial Intelligence, Newcastle University
Verified email at - Homepage
Cited by
Cited by
KEEL: a software tool to assess evolutionary algorithms for data mining problems
J Alcalá-Fdez, L Sanchez, S Garcia, MJ del Jesus, S Ventura, JM Garrell, ...
Soft Computing 13 (3), 307-318, 2009
Single-cell multi-omics analysis of the immune response in COVID-19
E Stephenson, G Reynolds, RA Botting, FJ Calero-Nieto, MD Morgan, ...
Nature medicine 27 (5), 904-916, 2021
Decoding human fetal liver haematopoiesis
DM Popescu, RA Botting, E Stephenson, K Green, S Webb, L Jardine, ...
Nature 574 (7778), 365-371, 2019
Developmental cell programs are co-opted in inflammatory skin disease
G Reynolds, P Vegh, J Fletcher, EFM Poyner, E Stephenson, I Goh, ...
Science 371 (6527), eaba6500, 2021
MRPR: A MapReduce solution for prototype reduction in big data classification
I Triguero, D Peralta, J Bacardit, S García, F Herrera
neurocomputing 150, 331-345, 2015
Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology
AL Swan, A Mobasheri, D Allaway, S Liddell, J Bacardit
Omics: a journal of integrative biology 17 (12), 595-610, 2013
ROSEFW-RF: the winner algorithm for the ECBDL’14 big data competition: an extremely imbalanced big data bioinformatics problem
I Triguero, S Del Río, V López, J Bacardit, JM Benítez, F Herrera
Knowledge-Based Systems 87, 69-79, 2015
The eukaryotic N-end rule pathway: conserved mechanisms and diverse functions
DJ Gibbs, J Bacardit, A Bachmair, MJ Holdsworth
Trends in Cell Biology 24 (10), 603-611, 2014
Pittsburgh genetics-based machine learning in the data mining era: Representations, generalization, and run-time
J Bacardit
PhD disertation, 2004
Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data
E Glaab, J Bacardit, JM Garibaldi, N Krasnogor
PloS one 7 (7), e39932, 2012
Prediction of human population responses to toxic compounds by a collaborative competition
F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ...
Nature biotechnology 33 (9), 933-940, 2015
Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets
GW Bassel, E Glaab, J Marquez, MJ Holdsworth, J Bacardit
The Plant Cell 23 (9), 3101-3116, 2011
Improving the scalability of rule-based evolutionary learning
J Bacardit, EK Burke, N Krasnogor
Memetic computing 1, 55-67, 2009
Automated individual pig localisation, tracking and behaviour metric extraction using deep learning
J Cowton, I Kyriazakis, J Bacardit
IEEE Access 7, 108049-108060, 2019
Data mining in learning classifier systems: comparing XCS with GAssist
J Bacardit, M Butz
Learning Classifier Systems, 282-290, 2007
Large scale data mining using genetics-based machine learning
J Bacardit, X Llorà
Proceedings of the 11th Annual Conference Companion on Genetic and …, 2009
Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system
J Bacardit, JM Garrell
International Workshop on Learning Classifier Systems, 59-79, 2003
A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women
N Lazzarini, J Runhaar, AC Bay-Jensen, CS Thudium, ...
Osteoarthritis and cartilage 25 (12), 2014-2021, 2017
Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system
J Bacardit, JM Garrell
Genetic and evolutionary computation conference, 1818-1831, 2003
Blood and immune development in human fetal bone marrow and Down syndrome
L Jardine, S Webb, I Goh, M Quiroga Londoño, G Reynolds, M Mather, ...
Nature 598 (7880), 327-331, 2021
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