Staff Profile
Professor Jaume Bacardit
Professor of Artificial Intelligence
- Telephone: +44 191 208 7784
- Personal Website: http://homepages.cs.ncl.ac.uk/jaume.bacardit/
- Address: School of Computing
Office 5.020, Urban Sciences Building,
Newcastle University
Newcastle upon Tyne
NE4 5TG
I am a Professor of Artificial Intelligence at the School of Computing of Newcastle University since 2017. I am affiliated to the Interdisciplinary Computing and Complex BioSystems (ICOS) research group. My areas of expertise are artificial intelligence, bioinformatics and biomedical data analytics.
Research
My research interests include the development of machine learning methods for large-scale problems and their application to challenging problems, mostly involving biological data.
Academic background
I received a BEng and MEng in Computer Engineering and a PhD in Computer Science from the Ramon Llull University in Barcelona, Spain in 1998, 2000 and 2004, respectively.
My PhD thesis involved the adaptation and application of a class of rule-based machine learning methods called Learning Classifier Systems to Data Mining tasks in terms of scalability, knowledge representations and generalisation capacity.
From 2005 to 2007 I was a postdoc at the University of Nottingham working on Protein Structure Prediction. From 2008 to 2013 I was a Lecturer in Bioinformatics at the University of Nottingham, and from 2014 to 2017 i was Senior Lecturer in Biodata Mining at Newcastle University.
Google Scholar: Click here.
My research interests include the development of machine learning methods for large-scale problems, the design of techniques to extract knowledge and improve the interpretability of machine learning algorithms, known as Explainable AI, and the application of machine learning to life sciences real-world problems.
I have led the data analytics efforts of several large biological/biomedical interdisciplinary consortiums: APPROACH (EU-IMI €15M, focusing on Osteoarthritis phenotype identification) and PORTABOLOMICS (£4.3M EPSRC Programme grant focusing on Engineering Biology).
My applied machine learning work at the interface with the life sciences has always been interdisciplinary, collaborating with data generators to make sense of their data, be it on plant science [1], animal behaviour [2,3,4], basic immunology [5,6,7], engineering biology [8,9] or Osteoarthritis [10,11,12,13].
Interpreting how machine learning models take decisions has been one of Bacardit’s active areas of research for many years, way before the term Explainable AI emerged. Most of his early work was done in the specific context of an application domains but these works motivated the generation of general-purpose methods for biological knowledge extraction of panels of biomarkers [14] or functional networks [15].
I have published papers on algorithmic advances to improve the scalability of machine learning methods [16], tackling challenges such as large dimensionality spaces [17], large sets of records [18], postprocessing operators [19] or using computational backends such as GPUs [20,21] or MapReduce [22,23].
I have 100+ peer-reviewed publications, 6900+ citations and an H-index of 39 (Google Scholar, as of March 2024).
- Module leader of CSC3432 - Biomedical Data Analytics and AI
- Lecturer of CSC3431 - Introduction to BioDesign and Natural Computing
- Lecturer of CSC8111/CSC8635 - Machine Learning
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Articles
- Huang Y, Wipat A, Bacardit J. Transcriptional biomarker discovery toward building a load stress reporting system for engineered Escherichia coli strains. Biotechnology and Bioengineering 2024, 121(1), 355-365.
- Wirth W, Maschek S, Marijnissen ACA, Lalande A, Blanco FJ, Berenbaum F, van de Stadt LA, Kloppenburg M, Haugen IK, Ladel CH, Bacardit J, Wisser A, Eckstein F, Roemer FW, Lafeber FPJG, Weinans HH, Jansen M. Test–retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort. Osteoarthritis and Cartilage 2023, 31(2), 238-248.
- Shirt-Ediss B, Connolly J, Elezgaray J, Torelli E, Navarro SA, Bacardit J, Krasnogor N. Reverse engineering DNA origami nanostructure designs from raw scaffold and staple sequence list. Computational and Structural Biotechnology Journal 2023, 21, 3615-3626.
- Jansen MP, Wirth W, Bacardit J, van Helvoort EM, Marijnissen ACA, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Ladel CH, Loef M, Lafeber FPJG, Welsing PM, Mastbergen SC, Roemer FW. Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort. Quantitative Imaging in Medicine and Surgery 2023, 13(5), 3298-3306.
- Taylor C, Guy J, Bacardit J. Estimating individual-level pig growth trajectories from group-level weight time series using machine learning. Computers and Electronics in Agriculture 2023, 208, 107790.
- Widera P, Welsing PMJ, Danso SO, Peelen S, Kloppenburg M, Loef M, Marijnissen AC, vanHelvoort EM, Blanco FJ, Magalhaes J, Berenbaum F, Haugen IK, Bay-Jensen AC, Mobasheri A, Ladel C, Loughlin J, Lafeber FPJG, Lalande A, Larkin J, Weinans H, Bacardit J. Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials. Osteoarthritis Cartilage Open 2023, 5(4), 100406.
- Iqbal S, Agarwal S, Purcell I, Murray A, Bacardit J, Allen J. Deep learning identification of coronary artery disease from bilateral finger photoplethysmography sensing: A proof-of-concept study. Biomedical Signal Processing and Control 2023, 86, 104993.
- Iqbal S, Bacardit J, Griffiths B, Allen J. Deep learning classification of systemic sclerosis from multi-site photoplethysmography signals. Frontiers in Physiology: Computational Physiology and Medicine Special Issue on PPG 2023, 14, 1242807.
- Iqbal S, Bacardit J, Griffiths B, Allen J. Deep learning classification of systemic sclerosis from multi-site photoplethysmography signals. Frontiers in Physiology 2023, 14, 1242807.
- Wang Q, Runhaar J, Kloppenburg M, Boers M, Bijlsma JWJ, Bacardit J, Bierma-Zeinstra SMA, The CREDO Experts Group. A machine learning approach reveals features related to clinicians' diagnosis of clinically relevant knee osteoarthritis. Rheumatology 2023, 62(8), 2732-2739.
- Huang Y, Sinha N, Wipat A, Bacardit J. A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis. Synthetic and Systems Biotechnology 2023, 8(1), 97-106.
- Danesh H, Steel DH, Hogg J, Ashtari F, Innes W, Bacardit J, Hurlbert A, Read JCA, Kafieh R. Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases. Translational Vision Science and Technology 2022, 11(10), 10.
- van Helvoort EM, Jansen MP, Marijnissen ACA, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Bay-Jensen AC, Ladel C, Lalande A, Larkin J, Loughlin J, Mobasheri A, Weinans HH, Widera P, Bacardit J, Welsing PMJ, Lafeber FPJG. Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort. Rheumatology 2022, 62(1), 147–157.
- Angelini F, Widera P, Mobasheri A, Blair J, Struglics A, Uebelhoer M, Henrotin Y, Marijnissen ACA, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Ladel C, Larkin J, Bay-Jensen AC, Bacardit J. Osteoarthritis endotype discovery via clustering of biochemical marker data. Annals of the Rheumatic Diseases 2022, 81(5), 666-675.
- Walker D, Ruane M, Bacardit J, Coleman S. Insight from data analytics in a facilities management company. Quality and Reliability Engineering International 2022, 38(3), 1416-1440.
- Darke P, Cassidy S, Catt M, Taylor R, Missier P, Bacardit J. Curating a longitudinal research resource using linked primary care EHR data-a UK Biobank case study. Journal of the American Medical Informatics Association : JAMIA 2022, 29(3), 546-552.
- Lam B, Catt M, Cassidy S, Bacardit J, Darke P, Butterfield S, Alshabrawy O, Trenell M, Missier P. Using wearable activity trackers to predict type 2 diabetes: Machine learning–based cross-sectional study of the UK Biobank accelerometer cohort. JMIR Diabetes 2021, 6(1), e23364.
- Rajgor A, Patel A, McCulloch D, Obara B, Bacardit J, McQueen A, Aboagye E, Ali T, O'Hara J, Hamilton D. The application of radiomics in laryngeal cancer. British Journal of Radiology 2021, 94(1128), 20210499.
- Sokolovsky A, Gross T, Bacardit J. Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow. PLoS ONE 2021, 16(2), e0246464.
- Reynolds G, Vegh P, Fletcher J, Poyner EFM, Stephenson E, Goh I, Botting RA, Huang N, Olabi B, Dubois A, Dixon D, Green K, Maunder D, Engelbert J, Efremova M, Polański K, Jardine L, Jones C, Ness T, Horsfall D, McGrath J, Carey C, Popescu D-M, Webb S, Wang X-N, Sayer B, Park J-E, Negri VA, Belokhvostova D, Lynch MD, McDonald D, Filby A, Hagai T, Meyer KB, Husain A, Coxhead J, Vento-Tormo R, Behjati S, Lisgo S, Villani A-C, Bacardit J, Jones PH, O'Toole EA, Ogg GS, Rajan N, Reynolds NJ, Teichmann SA, Watt FM, Haniffa M. Developmental cell programs are co-opted in inflammatory skin disease. Science 2021, 371(6527), eaba6500.
- Little B, Alshabrawy O, Stow D, Ferrier IN, McNaney R, Jackson DG, Ladha K, Ladha C, Ploetz T, Bacardit J, Olivier P, Gallagher P, OBrien JT. Deep learning-based automated speech detection as a marker of social functioning in late-life depression. Psychological Medicine 2021, 51(9), 1441-1450.
- Huang Y, Smith W, Harwood C, Wipat A, Bacardit J. Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis. Sensors 2021, 21(7), 2436.
- Jardine L, Webb S, Goh I, Quiroga Londono M, Reynolds G, Mather M, Olabi B, Stephenson E, Botting RA, Horsfall D, Engelbert J, Maunder D, Mende N, Murnane C, Dann E, McGrath J, King H, Kucinski I, Queen R, Carey CD, Shrubsole C, Poyner E, Acres M, Jones C, Ness T, Coulthard R, Elliott N, O'Byrne S, Haltalli MLR, Lawrence JE, Lisgo S, Balogh P, Meyer KB, Prigmore E, Ambridge K, Jain MS, Efremova M, Pickard K, Creasey T, Bacardit J, Henderson D, Coxhead J, Filby A, Hussain R, Dixon D, McDonald D, Popescu D-M, Kowalczyk MS, Li B, Ashenberg O, Tabaka M, Dionne D, Tickle TL, Slyper M, Rozenblatt-Rosen O, Regev A, Behjati S, Laurenti E, Wilson NK, Roy A, Gottgens B, Roberts I, Teichmann SA, Haniffa M. Blood and immune development in human fetal bone marrow and Down syndrome. Nature 2021, 598, 327-331.
- van Helvoort EM, Ladel C, Mastbergen S, Kloppenburg M, Blanco FJ, Haugen IK, Berenbaum F, Bacardit J, Widera P, Welsing PMJ, Lafeber F. Baseline clinical characteristics of predicted structural and pain progressors in the IMI-APPROACH knee OA cohort. RMD Open 2021, 7(3), e001759.
- Widera P, Welsing PM, Ladel C, Loughlin J, Lafeber FP, Petit-Dop F, Larkin J, Weinans H, Mobasheri A, Bacardit J. Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data. Scientific Reports 2020, 10, 8427.
- vanHelvoort EM, vanSpil WE, Jansen MP, Welsing PMJ, Kloppenburg M, Loef M, Blanco FJ, Haugen IK, Berenbaum F, Bacardit J, Ladel CH, Loughlin J, BayJensen AC, Mobasheri A, Larkin J, Boere J, Weinans HH, Lalande A, Marijnissen ACA, Lafeber FPJG. Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers. BMJ Open 2020, 10, e035101.
- Franco MA, Krasnogor N, Bacardit J. Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure Identification. IEEE Computational Intelligence Magazine 2020, 15(3), 28-46.
- Alameer A, Kyriazakis I, Dalton H, Miller AL, Bacardit J. Automatic recognition of feeding and foraging behaviour in pigs using deep learning. Biosystems Engineering 2020, 197, 91-104.
- Alameer A, Kyriazakis I, Bacardit J. Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs. Scientific Reports 2020, 10, 13665.
- Kueffner R, Zach N, Bronfeld M, Norel R, Atassi N, Balagurusamy V, Di Camillo B, Chio A, Cudkowicz M, Dillenberger D, Garcia-Garcia J, Hardiman O, Hoff B, Knight J, Leitner ML, Li G, Mangravite L, Norman T, Wang L, Xiao J, Fang W-C, Peng J, Yang C, Chang H-J, Stolovitzky G, Alkallas R, Anghel C, Avril J, Bacardit J, Balser B, Balser J, Bar-Sinai Y, Ben-David N, Ben-Zion E, Bliss R, Cai J, Chernyshev A, Chiang J-H, Chicco D, Corriveau BAN, Dai J, Deshpande Y, Desplats E, Durgin JS, Espiritu SMG, Fan F, Fevrier P, Fridley BL, Godzik A, Golinska A, Gordon J, Graw S, Guo Y, Herpelinck T, Hopkins J, Huang B, Jacobsen J, Jahandideh S, Jeon J, Ji W, Jung K, Karanevich A, Koestler DC, Kozak M, Kurz C, Lalansingh C, Larrieu T, Lazzarini N, Lerner B, Lesinski W, Liang X, Lin X, Lowe J, Mackey L, Meier R, Min W, Mnich K, Nahmias V, Noel-Macdonnell J, O'donnell A, Paadre S, Park J, Polewko-Klim A, Raghavan R, Rudnicki W, Saghapour E, Salomond J-B, Sankaran K, Sendorek D, Sharan V, Shiah Y-J, Sirois J-K, Sumanaweera DN, Usset J, Vang YS, Vens C, Wadden D, Wang D, Wong WC, Xie X, Xu Z, Yang H-T, Yu X, Zhang H, Zhang L, Zhang S, Zhu S. Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach. Scientific Reports 2019, 9, 690.
- Smith WS, Coleman S, Bacardit J, Coxon S. Insight from data analytics with an automotive aftermarket SME. Quality and Reliability Engineering International 2019, 35(5), 1396-1407.
- Popescu DM, Botting RA, Stephenson E, Green K, Webb S, Jardine L, Calderbank EF, Polanski K, Goh I, Efremova M, Acres M, Maunder D, Vegh P, Gitton Y, Park JE, Vento-Tormo R, Miao Z, Dixon D, Rowell R, McDonald D, Fletcher J, Poyner E, Reynolds G, Mather M, Moldovan C, Mamanova L, Greig F, Young MD, Meyer KB, Lisgo S, Bacardit J, Fuller A, Millar B, Innes B, Lindsay S, Stubbington MJT, Kowalczyk MS, Li B, Ashenberg O, Tabaka M, Dionne D, Tickle TL, Slyper M, Rozenblatt-Rosen O, Filby A, Carey P, Villani AC, Roy A, Regev A, Chédotal A, Roberts I, Göttgens B, Behjati S, Laurenti E, Teichmann SA, Haniffa M. Decoding human fetal liver haematopoiesis. Nature 2019, 574, 365–371.
- Cowton J, Kyriazakis I, Bacardit J. Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning. IEEE Access 2019, 7, 108049-108060.
- Smith W, Coleman S, Bacardit J, Coxon S. How data can change the automotive aftermarket. Focus 2018, (October), 30-32.
- Keasar C, McGuffin LJ, Wallner B, Chopra G, Adhikari B, Bhattacharya D, Blake L, Bortot LO, Cao R, Dhanasekaran BK, Dimas I, Faccioli RA, Faraggi E, Ganzynkowicz R, Ghosh S, Ghosh S, Gieldon A, Golon L, He Y, Heo L, Hou J, Khan M, Khatib F, Khoury GA, Kieslich C, Kim DE, Krupa P, Lee GR, Li H, Li J, Lipska A, Liwo A, Maghrabi AHA, Mirdita M, Mirzaei S, Mozolewska MA, Onel M, Ovchinnikov S, Shah A, Shah U, Sidi T, Sieradzan AK, Slusarz M, Slusarz R, Smadbeck J, Tamamis P, Trieber N, Wirecki T, Yin Y, Zhang Y, Bacardit J, Baranowski M, Chapman N, Cooper S, Defelicibus A, Flatten J, Koepnick B, Popovic Z, Zaborowski B, Baker D, Cheng J, Czaplewski C, Delbem ACB, Floudas C, Kloczkowski A, Oldziej S, Levitt M, Scheraga H, Seok C, Soding J, Vishveshwara S, Xu D, Caglar A, Coral A, MacMillan A, Lubow A, Failer B, Kestemont B, Landers CR, Painter CR, Garnier C, Sellin C, Janz D, Wheeler DC, Simon V, Flear DM, Croze E, McIlvaine GV, Beecher G, Lawrie G, Ykman G, Feldmann H, Fuentes HK, Terumasa H, Kovanecz I, Longino JA, Nijland JH, Diderich JA, Canfield JM, Eriksson J, Slone JD, Appel JG, Mitchell J, Mitch J, Loots-Boiy J, Brownlee JM, Wilson K, Clayton KT, Deford KE, Abbey KJ, Withers L, Wei L, Ives L, Miller LA, Carpenter L, Sharma MG, Ricci M, Binfield MS, Davids MJ, Gaebel M, Cassidy MD, Fagiola M, Pfutzenreuter M, Barlow N, Triggiani PJ, Innes RBM, Leduc R, Dos Santos Gomes RLC, Morneau RLR, Zaccanelli SJ, Kleinfelter SC, Van Der Laan TJA, Bausewein T, George TJ, Mikhail V, Barmettler W, Crivelli SN. An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Scientific Reports 2018, 8(1), 9939.
- Cowton J, Kyriazakis I, Ploetz T, Bacardit J. A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors. Sensors 2018, 18(8), 2521.
- Garcia-Piquer A, Bacardit J, Fornells A, Golobardes E. Scaling-up multiobjective evolutionary clustering algorithms using stratification. Pattern Recognition Letters 2017, 93, 69-77.
- Lazzarini N, Bacardit J. RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers. BMC Bioinformatics 2017, 18, 322.
- Lazzarini N, Runhaar J, Bay-Jensen AC, Thudium CS, Bierma-Zeinstra SMA, Henrotin Y, Bacardit J. A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. Osteoarthritis and Cartilage 2017, 25(12), 2014-2021.
- Franco MA, Bacardit J. Large-scale experimental evaluation of GPU strategies for evolutionary machine learning. Information Sciences 2016, 330, 385–402.
- Gutierrez PD, Lastra M, Bacardit J, Benitez JM, Herrera F. GPU-SME-kNN: Scalable and memory efficient kNN and lazy learning using GPUs. Information Sciences 2016, 373, 165-182.
- Lazzarini N, Widera P, Williamson S, Heer R, Krasnogor N, Bacardit J. Functional networks inference from machine learning models. BioData Mining 2016, 9, 28.
- Triguero I, del Rio S, Lopez V, Bacardit J, Benitez JM, Herrera F. ROSEFW-RF: The winner algorithm for the ECBDL'14 big data competition: An extremely imbalanced big data bioinformatics problem. Knowledge-Based Systems 2015, 87, 69-79.
- Eduati F, Mangravite L, Wang T, Tang H, Bare J, Huang R, Norman T, Kellen M, Menden M, Yang J, Zhan X, Zhong R, Xiao G, Xia M, Abdo N, Kosyk O, Eduati F, Bare J, Norman T, Kellen M, Menden M, Friend S, Stolovitzky G, Dearry A, Tice R, Huang R, Xia M, Simeonov A, Abdo N, Kosyk O, Rusyn I, Wright F, Wang T, Tang H, Zhan X, Yang J, Zhong R, Xiao G, Xie Y, Tang H, Yang J, Wang T, Xiao G, Xie Y, Alaimo S, Amadoz A, Ammad-Ud-din M, Azencott C, Bacardit J, Barron P, Bernard E, Beyer A, Bin S, van-Bömmel A, Borgwardt K, Brys A, Caffrey B, Chang J, Chang J, Christodoulou E, Clément-Ziza M, Cohen T, Cowherd M, Demeyer S, Dopazo J, Elhard J, Falcao A, Ferro A, Friedenberg D, Giugno R, Gong Y, Gorospe J, Granville C, Grimm D, Heinig M, Hernansaiz R, Hochreiter S, Huang L, Huska M, Jiao Y, Klambauer G, Kuhn M, Kursa M, Kutum R, Lazzarini N, Lee I, Leung M, Lim W, Liu C, López F, Mammana A, Mayr A, Michoel T, Mongiovì M, Moore J, Narasimhan R, Opiyo S, Pandey G, Peabody A, Perner J, Pulvirenti A, Rawlik K, Reinhardt S, Riffle C, Ruderfer D, Sander A, Savage R, Scornet E, Sebastian-Leon P, Sharan R, Simon-Gabriel C, Stoven V, Sun J, Tang H, Teixeira A, Tenesa A, Vert J, Vingron M, Wang T, Walter T, Whalen S, Wisniewska Z, Wu Y, Xiao G, Xie Y, Xu H, Yang J, Zhan X, Zhang S, Zhao J, Zheng W, Zhong R, Ziwei D, Friend S, Dearry A, Simeonov A, Tice R, Rusyn I, Wright F, Stolovitzky G, Xie Y, Saez-Rodriguez J. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology 2015, 33, 933-940.
- Triguero I, Peralta D, Bacardit J, García S, Herrera F. MRPR: A MapReduce solution for prototype reduction in big data classification. Neurocomputing 2015, 150(PA), 331-345.
- Martinez-Ballesteros M, Bacardit J, Troncoso A, Riquelme JC. Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets. Integrated Computer-Aided Engineering 2015, 22(1), 21-39.
- Swan AL, Stekel DJ, Hodgman C, Allaway D, Alqahtani MH, Mobasheri A, Bacardit J. A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data. BMC Genomics 2015, 16(Suppl 1), S2.
- Khoury GA, Liwo A, Khatib F, Zhou H, Chopra G, Bacardit J, Bortot LO, Faccioli RA, Deng X, He Y, Krupa P, Li J, Mozolewska MA, Sieradzan AK, Smadbeck J, Wirecki T, Cooper S, Flatten J, Xu K, Baker D, Cheng J, Delbem ACB, Floudas CA, Keasar C, Levitt M, Popović Z, Scheraga HA, Skolnick J, Crivelli SN, Foldit Players. WeFold: A coopetition for protein structure prediction. Proteins: Structure, Function, and Bioinformatics 2014, 82(9), 1850-1868.
- Gibbs DJ, Bacardit J, Bachmair A, Holdsworth MJ. The eukaryotic N-end rule pathway: conserved mechanisms and diverse functions. Trends in Cell Biology 2014, 24(10), 603-611.
- Alkurashi MM, May ST, Kong K, Bacardit J, Haig D, Elsheikha HM. Susceptibility to experimental infection of the invertebrate locusts (Schistocerca gregaria) with the apicomplexan parasite Neospora caninum. PeerJ 2014, 2, e674.
- Garcia-Piquer A, Fornells A, Bacardit J, Orriols-Puig A, Golobardes E. Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering. IEEE Transactions on Evolutionary Computation 2014, 18(1), 36-53.
- Blakes J, Raz O, Feige U, Bacardit J, Widera P, Ben-Yehezkel T, Shapiro E, Krasnogor N. Heuristic for Maximizing DNA Reuse in Synthetic DNA Library Assembly. ACS Synthetic Biology 2014, 3(8), 529-542.
- Bacardit J, Widera P, Lazzarini N, Krasnogor N. Hard Data Analytics Problems Make for Better Data Analysis Algorithms: Bioinformatics as an Example. Big Data 2014, 2(3), 164-176.
- Bacardit J, Llorà X. Large-scale data mining using genetics-based machine learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2013, 3(1), 37-61.
- Calian DA, Bacardit J. Integrating memetic search into the BioHEL evolutionary learning system for large-scale datasets. Memetic Computing 2013, 5(2), 95-130.
- Franco MA, Krasnogor N, Bacardit J. GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning. Soft Computing 2013, 17(6), 953-981.
- Swan AL, Mobasheri A, Allaway D, Liddell S, Bacardit J. Application of Machine Learning to Proteomics Data: Classification Biomarker Identification in Postgenomics Biology. OMICS: A Journal of Integrative Biology 2013, 17(12), 595-610.
- Swan AL, Hillier KL, Smith JR, Allaway D, Liddell S, Bacardit J, Mobasheri A. Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning. BMC Musculoskeletal Disorders 2013, 14, 349.
- Glaab E, Bacardit J, Garibaldi JM, Krasnogor N. Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization;Sample Classification of Cancer Gene Expression Data. PLoS ONE 2012, 7(7), e39932.
- Fainberg HP, Bodley K, Bacardit J, Li D, Wessely F, Mongan NP, Symonds ME, Clarke L, Mostyn A. Reduced Neonatal Mortality in Meishan Piglets: A Role for Hepatic Fatty Acids?. PLoS ONE 2012, 7(11), e49101.
- Bacardit J, Widera P, Márquez-Chamorro A, Divina F, Aguilar-Ruiz JS, Krasnogor N. Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features. Bioinformatics 2012, 28(19), 2441-2448.
- Franco MA, Krasnogor N, Bacardit J. Analysing BioHEL using challenging boolean functions. Evolutionary Intelligence 2012, 5(2), 87-102.
- Bassel GW, Glaab E, Marquez J, Holdsworth MJ, Bacardit J. Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets. Plant Cell 2011, 23(9), 3101-3116.
- Smith RE, Jiang MK, Bacardit J, Stout M, Krasnogor N, Hirst JD. A learning classifier system with mutual-information-based fitness. Evolutionary Intelligence 2010, 3(1), 31-50.
- Stout M, Bacardit J, Hirst JD, Smith RE, Krasnogor N. Prediction of topological contacts in proteins using learning classifier systems. Soft Computing 2009, 13(3), 245-258.
- Bacardit J, Krasnogor N. Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems. Evolutionary Computation Journal 2009, 17(3), 307-342.
- Alcala-Fdez J, Sanchez L, Garcia S, delJesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernandez JC, Herrera F. KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Computing 2009, 13(3), 307-318.
- Bacardit J, Burke EK, Krasnogor N. Improving the scalability of rule-based evolutionary learning. Memetic Computing 2009, 1(1), 55-67.
- Bacardit J, Stout M, Hirst JD, Valencia A, Smith RE, Krasnogor N. Automated Alphabet Reduction for Protein Datasets. BMC Bioinformatics 2009, 10, 6.
- Stout M, Bacardit J, Hirst JD, Krasnogor N. Prediction of recursive convex hull class assignments for protein residues. Bioinformatics 2008, 24(7), 916-923.
- Teixido M, Belda I, Roselló X, González S, Fabre M, Llorá X, Bacardit J, Garrell JM, Vilaró S, Albericio F, Giralt E. Development of a Genetic Algorithm to Design;Identify Peptides that can Cross the Blood-Brain Barrier. QSAR & Combinatorial Science 2003, 22(7), 745-753.
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Book Chapters
- Bacardit J, Bernado-Mansilla E, Butz MV. Learning Classifier Systems: Looking Back;Glimpsing Ahead. In: Learning Classifier Systems. Springer, 2008, pp.1-21.
- Bacardit J, Krasnogor N. Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System. In: Learning Classifier Systems. Springer, 2008, pp.255-268.
- Bacardit J, Stout M, Hirst JD, Krasnogor N. Data Mining in Proteomics with Learning Classifier Systems. In: Learning Classifier Systems in Data Mining. Springer, 2008, pp.17-46.
- Bacardit J, Goldberg DE, Butz MV. Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule. In: Learning Classifier Systems. Springer, 2007, pp.291-307.
- Bacardit J, Butz MV. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. In: Learning Classifier Systems. Springer, 2007, pp.282-290.
- Bacardit J, Garrell JM. Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System. In: Learning Classifier Systems. Springer, 2007, pp.59-79.
- Stout M, Bacardit J, Hirst JD, Krasnogor N, Blazewicz J. From HP Lattice Models to Real Proteins: Coordination Number Prediction Using Learning Classifier Systems. In: Applications of Evolutionary Computing. Springer, 2006, pp.208-220.
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Conference Proceedings (inc. Abstracts)
- Shirt-Ediss B, Connolly J, Torelli E, Navarro S, Elezgaray J, Bacardit J, Krasnogor N. Multi-Objective Sequence Selection for Scaffolded Origami Nanostructures. In: 28th International Conference on DNA Computing and Molecular Programming. 2022, Albuquerque, NM, USA: University of New Mexico.
- Connolly J, Shirt-Ediss B, Torelli E, Hobbs L, Bacardit J, Krasnogor N. NAOMEE: Nucleic Acid Origami Minimal Exchange Format. In: Unconventional Computation and Natural Computation Conference 21, UCNC21. 2021, Aalto University, Espoo, Finland.
- Fellermann H, Penn AS, Fuchslin RM, Bacardit J, Goni-Moreno A. Towards low-carbon conferencing: Acceptance of virtual conferencing solutions and other sustainability measures in the ALIFE community. In: Proceedings of the 2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019. 2019, Newcastle upon Tyne: MIT Press.
- Fellermann H, Bacardit J, Goni-Moreno A, Fuchslin RM. Artificial life in a challenged world. In: Proceedings of the 2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019. 2019, Newcastle upon Tyne, UK: MIT Press.
- Baron S, Lazzarini N, Bacardit J. Characterising the influence of rule-based knowledge representations in biological knowledge extraction from transcriptomics data. In: EvoApplications 2017: 20th European Conference on the Applications of Evolutionary Computation. 2017, Amsterdam: Springer.
- Coopamootoo KPL, Gross T. Mental models for usable privacy: A position paper. In: Second International Conference on Human Aspects of Information Security, Privacy, and Trust (HAS 2014). 2014, Crete, Greece: Springer Verlag.
- Triguero I, Peralta D, Bacardit J, Garcia S, Herrera F. A combined MapReduce-windowing two-level parallel scheme for evolutionary prototype generation. In: 2014 IEEE Congress on Evolutionary Computation (CEC). 2014, Beijing, China: IEEE.
- Franco MA, Krasnogor N, Bacardit J. Post-processing operators for decision lists. In: Fourteenth International Conference on Genetic and Evolutionary Computation - GECCO '12. 2012.
- Franco MA, Krasnogor N, Bacardit J. Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding. In: 13th Annual Conference on Genetic and Evolutionary Computation - GECCO '11. 2011.
- Franco MA, Krasnogor N, Bacardit J. Speeding up the evaluation of evolutionary learning systems using GPGPUs. In: 12th Annual Conference on Genetic and Evolutionary Computation. 2010.
- Bacardit J, Krasnogor N. A mixed discrete-continuous attribute list representation for large scale classification domains. In: 11th Annual Conference on Genetic and Evolutionary Computation - GECCO '09. 2009.
- Bacardit J, Stout M, Hirst JD, Sastry K, Llorà X, Krasnogor N. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In: 9th Annual Conference on Genetic and Evolutionary Computation - GECCO '07. 2007.
- Bacardit J, Krasnogor N. Smart crossover operator with multiple parents for a Pittsburgh learning classifier system. In: 8th Annual Conference on Genetic and Evolutionary Computation - GECCO '06. 2006.
- Stout M, Bacardit J, Hirst JD, Blazewicz J, Krasnogor N. Prediction Of Residue Exposure And Contact Number For Simplified Hp Lattice Model Proteins Using Learning Classifier Systems. In: 7th International FLINS Conference on Applied Artificial Intelligence. 2006.
- Bacardit J, Stout M, Krasnogor N, Hirst JD, Blazewicz J. Coordination number prediction using learning classifier systems: performance and interpretability. In: 8th Annual Conference on Genetic and Evolutionary Computation - GECCO '06. 2006.
- Bacardit J. Analysis of the initialization stage of a Pittsburgh approach learning classifier system. In: 7th Annual Conference on Genetic and Evolutionary Computation - GECCO '05. 2005.
- Bacardit J, Goldberg DE, Butz MV, Llorà X, Garrell JM. Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy. In: Parallel Problem Solving from Nature - PPSN VIII. 2004.
- Aguilar-Ruiz J, Bacardit J, Divina F. Experimental Evaluation of Discretization Schemes for Rule Induction. In: 6th Annual Conference on Genetic and Evolutionary Computation - GECCO '04. 2004.
- Bacardit J, Garrell JM. Analysis and Improvements of the Adaptive Discretization Intervals Knowledge Representation. In: 5th Annual Conference on Genetic and Evolutionary Computation - GECCO '03. 2004.
- Bacardit J, Garrell JM. Evolving Multiple Discretizations with Adaptive Intervals for a Pittsburgh Rule-Based Learning Classifier System. In: Genetic and Evolutionary Computation Conference — GECCO 2003. 2003, Springer.
- Bacardit J, Garrell JM. Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System. In: Iberoamerican Conference in Artificial Intelligence - IBERAMIA2002. 2002, Springer.
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Edited Books
- Fellermann H, Bacardit J, Goñi-Moreno A, Füchslin RM, ed. The 2019 Conference on Artificial Life. MIT Press, 2019.
- Bacardit J, Browne W, Drugowitsch J, Bernadó-Mansilla E, Butz MV, ed. Learning Classifier Systems. Springer, 2010.
- Bacardit J, Bernadó-Mansilla E, Butz MV, Kovacs T, Llorà X, Takadama K, ed. Learning Classifier Systems. 2008.
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Reviews
- Hannani MT, Thudium CS, Karsdal MA, Ladel C, Mobasheri A, Uebelhoer M, Larkin J, Bacardit J, Struglics A, Bay-Jensen A-C. From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers. Expert Review of Molecular Diagnostics 2024, 24(1-2), 23-38.
- Schmidt L, Mohamed S, Meader N, Bacardit J, Craig D. Automated data analysis of unstructured grey literature in health research: A mapping review. Research Synthesis Methods 2024, 15(2), 178-197.