Ross D. King
University of Cambridge, Department of Chemical Engineering and Biotechnology, UK
Chalmers University of Technology, Gothenburg, Sweden
Aberystwyth University, UK
http://users.aber.ac.uk/rdk/
https://scholar.google.com/citations?user=BgZp7XcAAAAJ
https://dl.acm.org/profile/81100195115
https://zbmath.org/authors/?q=ai:king.ross-d
https://en.wikipedia.org/wiki/Ross_D._King
https://orcid.org/0000-0001-7208-4387
https://www.wikidata.org/entity/Q7369266
https://www.scopus.com/authid/detail.uri?authorId=7404501046
http://isni.org/isni/0000000124304509
https://viaf.org/viaf/9361715
https://id.loc.gov/authorities/names/nb2004305030
https://orkg.org/resource/R10007
https://www.wikidata.org/entity/Q775551
Abbi Abdel-Rehim
Oghenejokpeme I. Orhobor
Hang Lou
Hao Ni
Ross D. King
Protein-ligand binding affinity prediction exploiting sequence constituent homology.
2023
August
39
Bioinform.
8
https://doi.org/10.1093/bioinformatics/btad502
db/journals/bioinformatics/bioinformatics39.html#AbdelRehimOLNK23
Oghenejokpeme I. Orhobor
Nastasiya F. Grinberg
Larisa N. Soldatova
Ross D. King
Imbalanced regression using regressor-classifier ensembles.
1365-1387
2023
April
112
Mach. Learn.
4
https://doi.org/10.1007/s10994-022-06199-4
db/journals/ml/ml112.html#OrhoborGSK23
Yuxuan Wang
Ross D. King
Extrapolation is Not the Same as Interpolation.
277-292
2023
DS
https://doi.org/10.1007/978-3-031-45275-8_19
conf/dis/2023
db/conf/dis/dis2023.html#WangK23
Filip Kronström
Alexander H. Gower
Ievgeniia A. Tiukova
Ross D. King
RIMBO - An Ontology for Model Revision Databases.
523-534
2023
DS
https://doi.org/10.1007/978-3-031-45275-8_35
conf/dis/2023
db/conf/dis/dis2023.html#KronstromGTK23
Alexander H. Gower
Konstantin Korovin
Daniel Brunnsåker
Ievgeniia A. Tiukova
Ross D. King
LGEM+: A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction.
628-643
2023
DS
https://doi.org/10.1007/978-3-031-45275-8_42
conf/dis/2023
db/conf/dis/dis2023.html#GowerKBTK23
Abbi Abdel-Rehim
Oghenejokpeme I. Orhobor
Hang Lou
Hao Ni
Ross D. King
Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction.
2023
abs/2301.07568
CoRR
https://doi.org/10.48550/arXiv.2301.07568
db/journals/corr/corr2301.html#abs-2301-07568
Stefan Kramer 0001
Mattia Cerrato
Saso Dzeroski
Ross D. King
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems.
2023
abs/2305.02251
CoRR
https://doi.org/10.48550/arXiv.2305.02251
db/journals/corr/corr2305.html#abs-2305-02251
Hector Zenil
Jesper Tegnér
Felipe S. Abrahão
Alexander Lavin
Vipin Kumar 0001
Jeremy G. Frey
Adrian Weller
Larisa N. Soldatova
Alan R. Bundy
Nicholas R. Jennings
Koichi Takahashi
Lawrence Hunter
Saso Dzeroski
Andrew Briggs
Frederick D. Gregory
Carla P. Gomes
Christopher K. I. Williams
Jon Rowe
James A. Evans
Hiroaki Kitano
Joshua B. Tenenbaum
Ross D. King
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence.
2023
abs/2307.07522
CoRR
https://doi.org/10.48550/arXiv.2307.07522
db/journals/corr/corr2307.html#abs-2307-07522
Adnan Mahmud
Oghenejokpeme I. Orhobor
Ross D. King
Extension of Transformational Machine Learning: Classification Problems.
2023
abs/2309.16693
CoRR
https://doi.org/10.48550/arXiv.2309.16693
db/journals/corr/corr2309.html#abs-2309-16693
Nada Al taweraqi
Ross D. King
Improved prediction of gene expression through integrating cell signalling models with machine learning.
323
2022
23
BMC Bioinform.
1
https://doi.org/10.1186/s12859-022-04787-8
db/journals/bmcbi/bmcbi23.html#taweraqiK22
Hugo Bellamy
Abbi Abdel-Rehim
Oghenejokpeme I. Orhobor
Ross D. King
Batched Bayesian Optimization for Drug Design in Noisy Environments.
3970-3981
2022
62
J. Chem. Inf. Model.
17
https://doi.org/10.1021/acs.jcim.2c00602
db/journals/jcisd/jcisd62.html#BellamyAOK22
Ross D. King
Oghenejokpeme I. Orhobor
Charles C. Taylor
Cross-validation is safe to use.
276
2021
3
Nat. Mach. Intell.
4
https://doi.org/10.1038/s42256-021-00332-z
db/journals/natmi/natmi3.html#KingOT21
Nastasiya F. Grinberg
Oghenejokpeme I. Orhobor
Ross D. King
An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat.
251-277
2020
109
Mach. Learn.
2
https://doi.org/10.1007/s10994-019-05848-5
https://www.wikidata.org/entity/Q90343683
db/journals/ml/ml109.html#GrinbergOK20
Oghenejokpeme I. Orhobor
Nickolai N. Alexandrov
Ross D. King
Predicting rice phenotypes with meta and multi-target learning.
2195-2212
2020
109
Mach. Learn.
11
https://doi.org/10.1007/s10994-020-05881-9
db/journals/ml/ml109.html#OrhoborAK20
Oghenejokpeme I. Orhobor
Larisa N. Soldatova
Ross D. King
Federated Ensemble Regression Using Classification.
325-339
2020
DS
https://doi.org/10.1007/978-3-030-61527-7_22
conf/dis/2020
db/conf/dis/dis2020.html#OrhoborSK20
Oghenejokpeme I. Orhobor
Joseph French
Larisa N. Soldatova
Ross D. King
Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology.
374-385
2020
DS
https://doi.org/10.1007/978-3-030-61527-7_25
conf/dis/2020
db/conf/dis/dis2020.html#OrhoborFSK20
Ainur Begalinova
Ross D. King
Barry Lennox
Riza Batista-Navarro
Self-supervised learning of object slippage: An LSTM model trained on low-cost tactile sensors.
191-196
2020
IRC
https://doi.org/10.1109/IRC.2020.00038
conf/irc/2020
db/conf/irc/irc2020.html#BegalinovaKLB20
Noureddin Sadawi
Iván Olier
Joaquin Vanschoren
Jan N. van Rijn
Jeremy Besnard
G. Richard J. Bickerton
Crina Grosan
Larisa N. Soldatova
Ross D. King
Multi-task learning with a natural metric for quantitative structure activity relationship learning.
68:1-68:13
2019
11
J. Cheminformatics
1
https://doi.org/10.1186/s13321-019-0392-1
https://www.wikidata.org/entity/Q94193275
db/journals/jcheminf/jcheminf11.html#SadawiOVRBBGSK19
Seetah ALSalamah
Riza Batista-Navarro
Ross D. King
Using Prior Knowledge to Facilitate Computational Reading of Arabic Calligraphy.
293-304
2019
IDEAL (2)
https://doi.org/10.1007/978-3-030-33617-2_30
conf/ideal/2019-2
db/conf/ideal/ideal2019-2.html#ALSalamahBK19
Iván Olier
Noureddin Sadawi
G. Richard J. Bickerton
Joaquin Vanschoren
Crina Grosan
Larisa N. Soldatova
Ross D. King
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery.
285-311
2018
107
Mach. Learn.
1
https://doi.org/10.1007/s10994-017-5685-x
https://www.wikidata.org/entity/Q93047984
db/journals/ml/ml107.html#OlierSBVGSK18
Ross D. King
Vlad Schuler Costa
Chris Mellingwood
Larisa N. Soldatova
Automating Sciences: Philosophical and Social Dimensions.
40-46
2018
37
IEEE Technol. Soc. Mag.
1
https://doi.org/10.1109/MTS.2018.2795097
db/journals/tasm/tasm37.html#KingCMS18
Seetah ALSalamah
Ross D. King
Towards the Machine Reading of Arabic Calligraphy: A Letters Dataset and Corresponding Corpus of Text.
19-23
2018
ASAR
https://doi.org/10.1109/ASAR.2018.8480228
conf/asar/2018
db/conf/asar/asar2018.html#SalamahK18
Oghenejokpeme I. Orhobor
Nickolai N. Alexandrov
Ross D. King
Predicting Rice Phenotypes with Meta-learning.
144-158
2018
DS
https://doi.org/10.1007/978-3-030-01771-2_10
conf/dis/2018
db/conf/dis/dis2018.html#OrhoborAK18
Tirtharaj Dash
Ashwin Srinivasan 0001
Lovekesh Vig
Oghenejokpeme I. Orhobor
Ross D. King
Large-Scale Assessment of Deep Relational Machines.
22-37
2018
ILP
https://doi.org/10.1007/978-3-319-99960-9_2
conf/ilp/2018
db/conf/ilp/ilp2018.html#DashSVOK18
Iván Olier
Oghenejokpeme I. Orhobor
Joaquin Vanschoren
Ross D. King
Transformative Machine Learning.
2018
abs/1811.03392
CoRR
http://arxiv.org/abs/1811.03392
db/journals/corr/corr1811.html#abs-1811-03392
Iván Olier
Noureddin Sadawi
G. Richard J. Bickerton
Joaquin Vanschoren
Crina Grosan
Larisa N. Soldatova
Ross D. King
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery.
2017
abs/1709.03854
CoRR
http://arxiv.org/abs/1709.03854
db/journals/corr/corr1709.html#abs-1709-03854
Andrew Currin
Konstantin Korovin
Maria Ababi
Katherine Roper
Douglas B. Kell
Philip J. Day
Ross D. King
Computing exponentially faster: Implementing a nondeterministic universal Turing machine using DNA.
2016
abs/1607.08078
CoRR
http://arxiv.org/abs/1607.08078
db/journals/corr/corr1607.html#CurrinKARKDK16
Ross D. King
On the Use of Computer Programs as Money.
2016
abs/1608.00878
CoRR
http://arxiv.org/abs/1608.00878
db/journals/corr/corr1608.html#King16
Robert Rozanski
Stefano Bragaglia
Oliver Ray
Ross D. King
Automating the Development of Metabolic Network Models.
145-156
2015
CMSB
https://doi.org/10.1007/978-3-319-23401-4_13
conf/cmsb/2015
db/conf/cmsb/cmsb2015.html#RozanskiBRK15
Iván Olier
Crina Grosan
Noureddin Sadawi
Larisa N. Soldatova
Ross D. King
Meta-QSAR: Learning How to Learn QSARs.
104-105
2015
MetaSel@PKDD/ECML
https://ceur-ws.org/Vol-1455/paper-11.pdf
conf/pkdd/2015metasel
db/conf/pkdd/metasel2015.html#OlierGSSK15
Larisa N. Soldatova
Daniel Nadis
Ross D. King
Piyali S. Basu
Emma Haddi
Véronique Baumlé
Nigel J. Saunders
Wolfgang Marwan
Brian B. Rudkin
EXACT2: the semantics of biomedical protocols.
S5
2014
15
BMC Bioinform.
S-14
https://doi.org/10.1186/1471-2105-15-S14-S5
https://www.wikidata.org/entity/Q34631635
db/journals/bmcbi/bmcbi15S.html#SoldatovaNKBHBSMR14
Ross D. King
Chuan Lu
An investigation into eukaryotic pseudouridine synthases.
2014
12
J. Bioinform. Comput. Biol.
4
https://doi.org/10.1142/S0219720014500152
https://www.wikidata.org/entity/Q38473578
db/journals/jbcb/jbcb12.html#KingL14
Fang Zhou
Claire Q
Ross D. King
Predicting the Geographical Origin of Music.
1115-1120
2014
ICDM
https://doi.org/10.1109/ICDM.2014.73
https://doi.ieeecomputersociety.org/10.1109/ICDM.2014.73
https://www.wikidata.org/entity/Q57459868
conf/icdm/2014
db/conf/icdm/icdm2014.html#ZhouQK14
Larisa N. Soldatova
Andrey Rzhetsky
Kurt De Grave
Ross D. King
Representation of probabilistic scientific knowledge.
S7
2013
4
J. Biomed. Semant.
S-1
http://www.jbiomedsem.com/content/4/S1/S7
db/journals/biomedsem/biomedsem4S.html#SoldatovaRGK13
Tanveer A. Faruquie
Ashwin Srinivasan 0001
Ross D. King
Topic Models with Relational Features for Drug Design.
45-57
2012
ILP
https://doi.org/10.1007/978-3-642-38812-5_4
https://www.wikidata.org/entity/Q57459896
conf/ilp/2012
db/conf/ilp/ilp2012.html#FaruquieSK12
Ross D. King
Numbers as Data Structures: The Prime Successor Function as Primitive
http://arxiv.org/abs/1104.3056
2011
CoRR
abs/1104.3056
db/journals/corr/corr1104.html#abs-1104-3056
George Macleod Coghill
Ross D. King
Ashwin Srinivasan 0001
Qualitative System Identification from Imperfect Data
http://arxiv.org/abs/1111.0051
2011
CoRR
abs/1111.0051
db/journals/corr/corr1111.html#abs-1111-0051
Paul D. Dobson
Kieran Smallbone
Daniel Jameson
Evangelos Simeonidis
Karin Lanthaler
Pinar Pir
Chuan Lu
Neil Swainston
Warwick B. Dunn
Paul Fisher
Duncan Hull
Marie Brown
Olusegun Oshota
Natalie J. Stanford
Douglas B. Kell
Ross D. King
Stephen G. Oliver
Robert D. Stevens
Pedro Mendes 0001
Further developments towards a genome-scale metabolic model of yeast.
145
2010
4
BMC Syst. Biol.
https://doi.org/10.1186/1752-0509-4-145
https://www.wikidata.org/entity/Q28748223
db/journals/bmcsb/bmcsb4.html#DobsonSJSLPLSDFHBOSKKOSM10
Da Qi
Ross D. King
Andrew L. Hopkins
G. Richard J. Bickerton
Larisa N. Soldatova
An Ontology for Description of Drug Discovery Investigations.
2010
7
J. Integr. Bioinform.
3
https://doi.org/10.2390/biecoll-jib-2010-126
https://www.wikidata.org/entity/Q38506830
db/journals/jib/jib7.html#QiKHBS10
Yihui Liu
Katherine Martin
Andrew Sparkes
Ross D. King
The Analysis of Yeast Cell Morphology Using a Robot Scientist.
10-14
2010
CIS
https://doi.org/10.1109/CIS.2010.10
https://doi.ieeecomputersociety.org/10.1109/CIS.2010.10
https://www.wikidata.org/entity/Q57459951
conf/cis/2010
db/conf/cis/cis2010.html#LiuMSK10
Oliver Ray
Ken E. Whelan
Ross D. King
Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition.
661-666
2010
CISIS
https://doi.org/10.1109/CISIS.2010.184
https://doi.ieeecomputersociety.org/10.1109/CISIS.2010.184
https://www.wikidata.org/entity/Q57459945
conf/cisis/2010
db/conf/cisis/cisis2010.html#RayWK10
Ross D. King
Amanda C. Schierz
Amanda Clare
Jem J. Rowland
Andrew Sparkes
Siegfried Nijssen
Jan Ramon
Inductive Queries for a Drug Designing Robot Scientist.
425-451
2010
Inductive Databases and Constraint-Based Data Mining
https://doi.org/10.1007/978-1-4419-7738-0_18
https://www.wikidata.org/entity/Q57459940
books/daglib/0025640
db/books/collections/DGP2010.html#KingSCRSNR10
Chuan Lu
Ross D. King
An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems.
2020-2027
2009
25
Bioinform.
16
https://doi.org/10.1093/bioinformatics/btp360
https://www.wikidata.org/entity/Q48273876
db/journals/bioinformatics/bioinformatics25.html#LuK09
Ross D. King
Jem J. Rowland
Wayne Aubrey
Maria Liakata
Magdalena Markham
Larisa N. Soldatova
Ken E. Whelan
Amanda Clare
Mike Young
Andrew Sparkes
Stephen G. Oliver
Pinar Pir
The Robot Scientist Adam.
46-54
2009
42
Computer
8
https://doi.org/10.1109/MC.2009.270
http://doi.ieeecomputersociety.org/10.1109/MC.2009.270
https://www.wikidata.org/entity/Q56784096
db/journals/computer/computer42.html#KingRALMSWCYSOP09
Oliver Ray
Ken E. Whelan
Ross D. King
A Nonmonotonic Logical Approach for Modelling and Revising Metabolic Networks.
825-829
2009
CISIS
https://doi.org/10.1109/CISIS.2009.175
https://doi.ieeecomputersociety.org/10.1109/CISIS.2009.175
https://www.wikidata.org/entity/Q57459962
conf/cisis/2009
db/conf/cisis/cisis2009.html#RayWK09
Oliver Ray
Ken E. Whelan
Ross D. King
Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data.
194-201
2009
ILP
https://doi.org/10.1007/978-3-642-13840-9_18
https://www.wikidata.org/entity/Q57459929
conf/ilp/2009
db/conf/ilp/ilp2009.html#RayWK09
Amanda C. Schierz
Ross D. King
Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds.
331-343
2009
PRIB
https://doi.org/10.1007/978-3-642-04031-3_29
https://www.wikidata.org/entity/Q57459975
conf/prib/2009
db/conf/prib/prib2009.html#SchierzK09
Ken E. Whelan
Ross D. King
Using a logical model to predict the growth of yeast.
2008
9
BMC Bioinform.
https://doi.org/10.1186/1471-2105-9-97
https://www.wikidata.org/entity/Q33319526
db/journals/bmcbi/bmcbi9.html#WhelanK08
George Macleod Coghill
Ashwin Srinivasan 0001
Ross D. King
Qualitative System Identification from Imperfect Data.
825-877
2008
32
J. Artif. Intell. Res.
https://doi.org/10.1613/jair.2374
db/journals/jair/jair32.html#CoghillSK08
Ashwin Srinivasan 0001
Ross D. King
Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming.
1475-1533
2008
9
J. Mach. Learn. Res.
https://dl.acm.org/doi/10.5555/1390681.1442781
db/journals/jmlr/jmlr9.html#SrinivasanK08
Ross D. King
Larisa N. Soldatova
Formalising Phylogenetic Experiments: Ontologies and Logical Inference.
59-62
2008
AAAI Spring Symposium: Symbiotic Relationships between Semantic Web and Knowledge Engineering
http://www.aaai.org/Library/Symposia/Spring/2008/ss08-07-008.php
conf/aaaiss/2008-7
db/conf/aaaiss/aaaiss2008-7.html#KingS08
Larisa N. Soldatova
Wayne Aubrey
Ross D. King
Amanda Clare
The EXACT description of biomedical protocols.
295-303
2008
ISMB
https://doi.org/10.1093/bioinformatics/btn156
https://www.wikidata.org/entity/Q38513299
conf/ismb/2008
db/conf/ismb/ismb2008.html#SoldatovaAKC08
Michael C. Riley
Amanda Clare
Ross D. King
Locational distribution of gene functional classes in Arabidopsis thaliana.
2007
8
BMC Bioinform.
https://doi.org/10.1186/1471-2105-8-112
https://www.wikidata.org/entity/Q21284232
db/journals/bmcbi/bmcbi8.html#RileyCK07
Robert Burbidge
Jem J. Rowland
Ross D. King
Nicholas T. Form
Benjamin J. Whitaker
Evolutionary Optimization of Three-Photon Absorption in Molecular Iodine.
96-100
2007
conf/cidm/2007
CIDM
https://doi.org/10.1109/CIDM.2007.368858
https://www.wikidata.org/entity/Q57460020
db/conf/cidm/cidm2007.html#BurbidgeRKFW07
Robert Burbidge
Jem J. Rowland
Ross D. King
Active Learning for Regression Based on Query by Committee.
209-218
2007
conf/ideal/2007
IDEAL
https://doi.org/10.1007/978-3-540-77226-2_22
db/conf/ideal/ideal2007.html#BurbidgeRK07
Simon M. Garrett
George Macleod Coghill
Ashwin Srinivasan 0001
Ross D. King
Learning Qualitative Models of Physical and Biological Systems.
248-272
2007
conf/dis/2007book
Computational Discovery of Scientific Knowledge
https://doi.org/10.1007/978-3-540-73920-3_12
db/conf/dis/book2007.html#GarrettCSK07
Ross D. King
Andreas Karwath
Amanda Clare
Luc Dehaspe
Logic and the Automatic Acquisition of Scientific Knowledge: An Application to Functional Genomics.
273-289
2007
conf/dis/2007book
Computational Discovery of Scientific Knowledge
https://doi.org/10.1007/978-3-540-73920-3_13
https://www.wikidata.org/entity/Q62661283
db/conf/dis/book2007.html#KingKCD07
Amanda Clare
Andreas Karwath
Helen Ougham
Ross D. King
Functional bioinformatics for Arabidopsis thaliana.
1130-1136
2006
22
Bioinform.
9
https://doi.org/10.1093/bioinformatics/btl051
https://www.wikidata.org/entity/Q45966118
db/journals/bioinformatics/bioinformatics22.html#ClareKOK06
Amanda Clare
Andreas Karwath
Helen Ougham
Ross D. King
Functional bioinformatics for Arabidopsis thaliana.
1674
2006
22
Bioinform.
13
https://doi.org/10.1093/bioinformatics/btl169
https://www.wikidata.org/entity/Q57460061
db/journals/bioinformatics/bioinformatics22.html#ClareKOK06a
Bård Buttingsrud
Einar Ryeng
Ross D. King
Bjørn K. Alsberg
Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships.
361-373
2006
20
J. Comput. Aided Mol. Des.
6
https://doi.org/10.1007/s10822-006-9058-y
https://www.wikidata.org/entity/Q57460082
db/journals/jcamd/jcamd20.html#ButtingsrudRKA06
Sébastien Ferré
Ross D. King
Finding Motifs in Protein Secondary Structure for Use in Function Prediction.
719-731
2006
13
J. Comput. Biol.
3
https://doi.org/10.1089/cmb.2006.13.719
https://www.wikidata.org/entity/Q48446694
db/journals/jcb/jcb13.html#FerreK06
Ashwin Srinivasan 0001
David Page
Rui Camacho
Ross D. King
Quantitative pharmacophore models with inductive logic programming.
65-90
2006
64
Mach. Learn.
1-3
https://doi.org/10.1007/s10994-006-8262-2
https://www.wikidata.org/entity/Q57460077
db/journals/ml/ml64.html#SrinivasanPCK06
Rui Camacho
Ross D. King
Ashwin Srinivasan 0001
Guest editorial.
145-147
2006
64
Mach. Learn.
1-3
https://doi.org/10.1007/s10994-006-9573-z
https://www.wikidata.org/entity/Q57460073
db/journals/ml/ml64.html#CamachoKS06
Larisa N. Soldatova
Amanda Clare
Andrew Sparkes
Ross D. King
An ontology for a Robot Scientist.
464-471
2006
conf/ismb/2006
ISMB (Supplement of Bioinformatics)
https://doi.org/10.1093/bioinformatics/btl207
https://www.wikidata.org/entity/Q34551843
db/conf/ismb/ismb2006.html#SoldatovaCSK06
Ross D. King
Simon M. Garrett
George Macleod Coghill
On the use of qualitative reasoning to simulate and identify metabolic pathway.
2017-2026
2005
21
Bioinform.
9
https://doi.org/10.1093/bioinformatics/bti255
https://www.wikidata.org/entity/Q45966566
db/journals/bioinformatics/bioinformatics21.html#KingGC05
Sébastien Ferré
Ross D. King
A Dichotomic Search Algorithm for Mining and Learning in Domain-Specific Logics.
1-32
2005
66
Fundam. Informaticae
1-2
http://content.iospress.com/articles/fundamenta-informaticae/fi66-1-2-02
db/journals/fuin/fuin66.html#FerreK05
Ross D. King
The Robot Scientist Project.
12
2005
conf/alt/2005
ALT
https://doi.org/10.1007/11564089_4
db/conf/alt/alt2005.html#King05
Ross D. King
Michael Young
Amanda Clare
Kenneth Whelan
Jem J. Rowland
The Robot Scientist Project.
16-25
2005
conf/dis/2005
Discovery Science
https://doi.org/10.1007/11563983_4
https://www.wikidata.org/entity/Q57460101
db/conf/dis/dis2005.html#KingYCWR05
Ross D. King
Applying Inductive Logic Programming to Predicting Gene Function.
57-68
2004
25
AI Mag.
1
db/journals/aim/aim25.html#King04
https://doi.org/10.1609/aimag.v25i1.1747
Ross D. King
Paul H. Wise
Amanda Clare
Confirmation of data mining based predictions of protein function.
1110-1118
2004
20
Bioinform.
7
https://doi.org/10.1093/bioinformatics/bth047
https://www.wikidata.org/entity/Q30892188
db/journals/bioinformatics/bioinformatics20.html#KingWC04
George Macleod Coghill
Simon M. Garrett
Ross D. King
Learning Qualitative Metabolic Models.
445-449
2004
conf/ecai/2004
ECAI
db/conf/ecai/ecai2004.html#CoghillGK04
Sébastien Ferré
Ross D. King
BLID: An Application of Logical Information Systems to Bioinformatics.
47-54
https://doi.org/10.1007/978-3-540-24651-0_5
2004
conf/icfca/2004
ICFCA
db/conf/icfca/icfca2004.html#FerreK04
Ross D. King
Mohammed Ouali
Poly-transformation.
99-107
https://doi.org/10.1007/978-3-540-28651-6_15
2004
conf/ideal/2004
IDEAL
db/conf/ideal/ideal2004.html#KingO04
Rui Camacho
Ross D. King
Ashwin Srinivasan 0001
Inductive Logic Programming, 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004, Proceedings
ILP
Lecture Notes in Computer Science
3194
Springer
2004
3-540-22941-8
https://doi.org/10.1007/b10011
db/conf/ilp/ilp2004.html
Hannu Toivonen
Ashwin Srinivasan 0001
Ross D. King
Stefan Kramer 0001
Christoph Helma
Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001.
1183-1193
2003
19
Bioinform.
10
db/journals/bioinformatics/bioinformatics19.html#ToivonenSKKH03
https://doi.org/10.1093/bioinformatics/btg130
https://www.wikidata.org/entity/Q45966997
Ashwin Srinivasan 0001
Ross D. King
Michael Bain 0001
An Empirical Study of the Use of Relevance Information in Inductive Logic Programming.
369-383
2003
4
J. Mach. Learn. Res.
http://jmlr.org/papers/v4/srinivasan03a.html
db/journals/jmlr/jmlr4.html#SrinivasanKB03
Amanda Clare
Ross D. King
Predicting gene function in Saccharomyces cerevisiae.
42-49
2003
conf/eccb/2003
ECCB
db/conf/eccb/eccb2003.html#ClareK03
Ross D. King
A Personal View of How Best to Apply ILP.
1
https://doi.org/10.1007/978-3-540-39917-9_1
2003
conf/ilp/2003
ILP
db/conf/ilp/ilp2003.html#King03
Amanda Clare
Ross D. King
Data Mining the Yeast Genome in a Lazy Functional Language.
19-36
2003
conf/padl/2003
PADL
https://doi.org/10.1007/3-540-36388-2_4
db/conf/padl/padl2003.html#ClareK03
David P. Enot
Ross D. King
Application of Inductive Logic Programming to Structure-Based Drug Design.
156-167
https://doi.org/10.1007/978-3-540-39804-2_16
2003
conf/pkdd/2003
PKDD
db/conf/pkdd/pkdd2003.html#EnotK03
Amanda Clare
Ross D. King
Machine learning of functional class from phenotype data.
160-166
2002
18
Bioinform.
1
db/journals/bioinformatics/bioinformatics18.html#ClareK02
https://doi.org/10.1093/bioinformatics/18.1.160
https://www.wikidata.org/entity/Q30672256
Andreas Karwath
Ross D. King
Homology Induction: the use of machine learning to improve sequence similarity searches.
11
2002
3
BMC Bioinform.
https://doi.org/10.1186/1471-2105-3-11
https://www.wikidata.org/entity/Q24806775
db/journals/bmcbi/bmcbi3.html#KarwathK02
Amanda Clare
Ross D. King
How well do we understand the clusters found in microarray data?
511-522
2002
2
Silico Biol.
4
http://content.iospress.com/articles/in-silico-biology/isb00069
http://www.bioinfo.de/isb/abstracts/02/0046.html
db/journals/isb/isb2.html#ClareK02
Janet Taylor
Ross D. King
Thomas Altmann
Oliver Fiehn
Application of metabolomics to plant genotype discrimination using statistics and machine learning.
241-248
2002
conf/eccb/2002
ECCB
db/conf/eccb/eccb2002.html#TaylorKAF02
Christoph Helma
Ross D. King
Stefan Kramer 0001
Ashwin Srinivasan 0001
The Predictive Toxicology Challenge 2000-2001.
107-108
2001
17
Bioinform.
1
db/journals/bioinformatics/bioinformatics17.html#HelmaKKS01
https://doi.org/10.1093/bioinformatics/17.1.107
Ross D. King
Andreas Karwath
Amanda Clare
Luc Dehaspe
The utility of different representations of protein sequence for predicting functional class.
445-454
2001
17
Bioinform.
5
db/journals/bioinformatics/bioinformatics17.html#KingKCD01
https://doi.org/10.1093/bioinformatics/17.5.445
https://www.wikidata.org/entity/Q42648658
Christopher H. Bryant
Stephen H. Muggleton
Stephen G. Oliver
Douglas B. Kell
Philip G. K. Reiser
Ross D. King
Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes.
1-36
2001
5
Electron. Trans. Artif. Intell.
B
http://www.ep.liu.se/ej/etai/2001/001/
db/journals/etai/etai5.html#BryantMOKRK01
Ross D. King
Nathalie Marchand-Geneste
Bjørn K. Alsberg
A quantum mechanics based representation of molecules for machine inference.
127-142
2001
5
Electron. Trans. Artif. Intell.
B
http://www.ep.liu.se/ej/etai/2001/008/
db/journals/etai/etai5.html#KingMA01
Philip G. K. Reiser
Ross D. King
Douglas B. Kell
Stephen H. Muggleton
Christopher H. Bryant
Stephen G. Oliver
Developing a Logical Model of Yeast Metabolism.
223-244
2001
5
Electron. Trans. Artif. Intell.
B
http://www.ep.liu.se/ej/etai/2001/013/
db/journals/etai/etai5.html#ReiserKKMBO01
Ross D. King
Ashwin Srinivasan 0001
Luc Dehaspe
Warmr: a data mining tool for chemical data.
173-181
2001
15
J. Comput. Aided Mol. Des.
2
https://doi.org/10.1023/A:1008171016861
https://www.wikidata.org/entity/Q30642554
db/journals/jcamd/jcamd15.html#KingSD01
Andreas Karwath
Ross D. King
An Automated ILP Server in the Field of Bioinformatics.
91-103
2001
conf/ilp/2001
ILP
https://doi.org/10.1007/3-540-44797-0_8
https://www.wikidata.org/entity/Q62661304
db/conf/ilp/ilp2001.html#KarwathK01
Amanda Clare
Ross D. King
Knowledge Discovery in Multi-label Phenotype Data.
42-53
2001
conf/pkdd/2001
PKDD
https://doi.org/10.1007/3-540-44794-6_4
db/conf/pkdd/pkdd2001.html#ClareK01
Ross D. King
Andreas Karwath
Amanda Clare
Luc Dehaspe
Genome scale prediction of protein functional class from sequence using data mining.
384-389
2000
conf/kdd/2000
KDD
https://doi.org/10.1145/347090.347172
https://www.wikidata.org/entity/Q62661313
db/conf/kdd/kdd2000.html#KingKCD00
Ashwin Srinivasan 0001
Ross D. King
Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes.
37-57
1999
3
Data Min. Knowl. Discov.
1
db/journals/datamine/datamine3.html#SrinivasanK99
https://doi.org/10.1023/A:1009815821645
Ashwin Srinivasan 0001
Ross D. King
Douglas W. Bristol
An assessment of submissions made to the Predictive Toxicology Evaluation Challenge.
270-275
1999
conf/ijcai/99
IJCAI
db/conf/ijcai/ijcai99.html#SrinivasanKB99
http://ijcai.org/Proceedings/99-1/Papers/040.pdf
Ashwin Srinivasan 0001
Ross D. King
Douglas W. Bristol
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment.
291-302
https://doi.org/10.1007/3-540-48751-4_27
1999
conf/ilp/1999
ILP
db/conf/ilp/ilp99.html#SrinivasanKB99
Ross D. King
Ashwin Srinivasan 0001
The discovery of indicator variables for QSAR using inductive logic programming.
571-580
1998
12
J. Comput. Aided Mol. Des.
6
http://ipsapp007.lwwonline.com/content/getfile/4830/3/2/abstract.htm
db/journals/jcamd/jcamd12.html#KingS98
Stephen H. Muggleton
Ashwin Srinivasan 0001
Ross D. King
Michael J. E. Sternberg
Biochemical Knowledge Discovery Using Inductive Logic Programming.
326-341
https://doi.org/10.1007/3-540-49292-5_29
1998
conf/dis/1998
Discovery Science
db/conf/dis/dis98.html#MuggletonSKS98
Luc Dehaspe
Hannu Toivonen
Ross D. King
Finding Frequent Substructures in Chemical Compounds.
30-36
1998
conf/kdd/1998
KDD
db/conf/kdd/kdd98.html#DehaspeTK98
http://www.aaai.org/Library/KDD/1998/kdd98-005.php
Ross D. King
Mansoor A. S. Saqi
Roger A. Sayle
Michael J. E. Sternberg
DSC: public domain protein secondary structure predication.
473-474
1997
13
Comput. Appl. Biosci.
4
db/journals/bioinformatics/bioinformatics13.html#KingSSS97
https://doi.org/10.1093/bioinformatics/13.4.473
https://www.wikidata.org/entity/Q60210313
Ross D. King
Ashwin Srinivasan 0001
The discovery of indicator variables for QSAR using inductive logic programming.
571-580
1997
11
J. Comput. Aided Mol. Des.
6
https://doi.org/10.1023/A:1007967728701
https://www.wikidata.org/entity/Q52247866
db/journals/jcamd/jcamd11.html#KingS97
Ashwin Srinivasan 0001
Ross D. King
Stephen H. Muggleton
Michael J. E. Sternberg
The Predictive Toxicology Evaluation Challenge.
4-9
1997
conf/ijcai/1997
IJCAI (1)
db/conf/ijcai/ijcai97.html#SrinivasanKMS97
http://ijcai.org/Proceedings/97-1/Papers/001.pdf
Ashwin Srinivasan 0001
Ross D. King
Stephen H. Muggleton
Michael J. E. Sternberg
Carcinogenesis Predictions Using ILP.
273-287
1997
conf/ilp/1997
ILP
db/conf/ilp/ilp97.html#SrinivasanKMS97
https://doi.org/10.1007/3540635149_56
https://www.wikidata.org/entity/Q57460200
Ashwin Srinivasan 0001
Stephen H. Muggleton
Michael J. E. Sternberg
Ross D. King
Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction.
277-299
1996
85
Artif. Intell.
1-2
db/journals/ai/ai85.html#SrinivasanMSK96
https://doi.org/10.1016/0004-3702(95)00122-0
https://www.wikidata.org/entity/Q29542414
Ross D. King
C. G. Angus
PM - protein music.
251-252
1996
12
Comput. Appl. Biosci.
3
db/journals/bioinformatics/bioinformatics12.html#KingA96
https://doi.org/10.1093/bioinformatics/12.3.251
Ashwin Srinivasan 0001
Ross D. King
Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity by Structural Attributes.
89-104
1996
conf/ilp/1996
Inductive Logic Programming Workshop
db/conf/ilp/ilp96.html#SrinivasanK96
https://doi.org/10.1007/3-540-63494-0_50
Ross D. King
Comparison of artificial intelligence methods for modeling pharmaceutical QSARS.
213-233
1995
9
Appl. Artif. Intell.
2
db/journals/aai/aai9.html#King95
https://doi.org/10.1080/08839519508945474
https://www.wikidata.org/entity/Q57460228
Ross D. King
Cao Feng
A. Sutherland
STALOG: Comparison of classification algorithms on large real-world problems.
289-333
1995
9
Appl. Artif. Intell.
3
db/journals/aai/aai9.html#KingFS95
https://doi.org/10.1080/08839519508945477
https://www.wikidata.org/entity/Q57460243
Ross D. King
Michael J. E. Sternberg
Ashwin Srinivasan 0001
Relating Chemical Activity to Structure: An Examination of ILP Successes.
411-433
1995
13
New Gener. Comput.
3&4
db/journals/ngc/ngc13.html#KingSS95
https://doi.org/10.1007/BF03037232
https://www.wikidata.org/entity/Q57460235
Michael J. E. Sternberg
Ross D. King
Ashwin Srinivasan 0001
Stephen H. Muggleton
Drug Design by Machine Learning.
328-338
1995
conf/mi/1995
Machine Intelligence 15
db/conf/mi/mi1995.html#SternbergKSM95
Jonathan D. Hirst
Ross D. King
Michael J. E. Sternberg
Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines.
405-420
1994
8
J. Comput. Aided Mol. Des.
4
db/journals/jcamd/jcamd8.html#HirstKS94
https://doi.org/10.1007/BF00125375
https://www.wikidata.org/entity/Q52373584
Jonathan D. Hirst
Ross D. King
Michael J. E. Sternberg
Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines.
421-432
1994
8
J. Comput. Aided Mol. Des.
4
db/journals/jcamd/jcamd8.html#HirstKS94a
https://doi.org/10.1007/BF00125376
https://www.wikidata.org/entity/Q52373580
Ross D. King
Inductive logic programming: techniques and applications by Nada Lavrac and Saso Dzeroski, Ellis Horwood, UK, 1993, pp 293, £39.95, ISBN 0-13-457870-8.
311-312
1994
9
Knowl. Eng. Rev.
3
https://doi.org/10.1017/S0269888900007037
db/journals/ker/ker9.html#King94
Ivan Bratko
Ross D. King
Applications of Inductive Logic Programming.
43-49
1994
5
SIGART Bull.
1
db/journals/sigart/sigart5.html#BratkoK94
https://doi.org/10.1145/181668.181678
Ross D. King
Dominic A. Clark
Jack Shirazi
Michael J. E. Sternberg
Inductive Logic Programming Used to Discover Topological Constraints in Protein Structures.
219-226
1994
conf/ismb/1994
ISMB
db/conf/ismb/ismb1994.html#KingCSS94
http://www.aaai.org/Library/ISMB/1994/ismb94-027.php
Ross D. King
Dominic A. Clark
Jack Shirazi
Michael J. E. Sternberg
Discovery of Protein Structural Constraints in a Deductive Database using Inductive Logic Programming.
275-302
1993
conf/mi/1993
Machine Intelligence 14
db/conf/mi/mi1993.html#KingCSS93
A. Sutherland
Bob Henery
Rafael Molina 0001
Charles C. Taylor
Ross D. King
Statistical Methods in Learning.
173-182
1992
conf/ipmu/1992
IPMU
db/conf/ipmu/ipmu1992.html#SutherlandHMTK92
https://doi.org/10.1007/3-540-56735-6_54
Michael J. E. Sternberg
R. A. Lewis
Ross D. King
Stephen H. Muggleton
Machine Learning and biomolecular modelling.
193-212
1992
conf/mi/1992
Machine Intelligence 13
db/conf/mi/mi1994.html#SternbergLKM94
Ross D. King
Bob Henery
Cao Feng
A. Sutherland
A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Network.
311-359
1992
conf/mi/1992
Machine Intelligence 13
db/conf/mi/mi1994.html#KingHFS94
Steffen Schulze-Kremer
Ross D. King
IPSA: Inductive Protein Structure Analysis.
513
1991
conf/ecml/1991
EWSL
db/conf/ecml/ewsl91.html#Schulze-KremerK91
https://doi.org/10.1007/BFb0017042
Ross D. King
An Inductive Learning Approach to the Problem of Predicting a Protein's Secondary Structure from Its Amino Acid Sequence.
230-250
1987
conf/ecml/1987
EWSL
db/conf/ecml/ewsl87.html#King87
Maria Ababi
Abbi Abdel-Rehim
Felipe S. Abrahão
Nickolai N. Alexandrov
Seetah ALSalamah
Bjørn K. Alsberg
Thomas Altmann
C. G. Angus
Wayne Aubrey
Michael Bain 0001
Piyali S. Basu
Riza Theresa Batista-NavarroRiza Batista-Navarro
Véronique Baumlé
Ainur Begalinova
Hugo Bellamy
Jeremy Besnard
G. Richard J. Bickerton
Stefano Bragaglia
Ivan Bratko
Andrew Briggs
Douglas W. Bristol
Marie Brown
Daniel Brunnsåker
Christopher H. Bryant
Alan R. Bundy
Robert Burbidge
Bård Buttingsrud
Rui Camacho
Mattia Cerrato
Amanda Clare
Dominic A. Clark
George Macleod Coghill
Vlad Schuler Costa
Andrew Currin
Tirtharaj Dash
Philip J. Day
Luc Dehaspe
Paul D. Dobson
Warwick B. Dunn
Saso Dzeroski
David P. Enot
James A. Evans
Tanveer A. Faruquie
Cao Feng
Sébastien Ferré
Oliver Fiehn
Paul Fisher
Nicholas T. Form
Joseph French
Jeremy G. Frey
Simon M. Garrett
Carla P. Gomes
Alexander H. Gower
Kurt De Grave
Frederick D. Gregory
Nastasiya F. Grinberg
Crina Grosan
Emma Haddi
Christoph Helma
Bob Henery
Jonathan D. Hirst
Andrew L. Hopkins
Duncan Hull
Lawrence Hunter
Daniel Jameson
Nicholas R. Jennings
Andreas Karwath
Douglas B. Kell
Hiroaki Kitano
Konstantin Korovin
Stefan Kramer 0001
Filip Kronström
Vipin Kumar 0001
Karin Lanthaler
Alexander Lavin
Barry Lennox
R. A. Lewis
Maria Liakata
Yihui Liu
Hang Lou
Chuan Lu
Adnan Mahmud
Nathalie Marchand-Geneste
Magdalena Markham
Katherine Martin
Wolfgang Marwan
Chris Mellingwood
Pedro Mendes 0001
Rafael Molina 0001
Stephen H. Muggleton
Daniel Nadis
Hao Ni
Siegfried Nijssen
Iván Olier
Stephen G. Oliver
Oghenejokpeme I. Orhobor
Olusegun Oshota
Mohammed Ouali
Helen Ougham
David Page
Pinar Pir
Claire Q
Da Qi
Jan Ramon
Oliver Ray
Philip G. K. Reiser
Jan N. van Rijn
Michael C. Riley
Katherine Roper
Jon Rowe
Jem J. Rowland
Robert Rozanski
Brian B. Rudkin
Einar Ryeng
Andrey Rzhetsky
Noureddin Sadawi
Mansoor A. S. Saqi
Nigel J. Saunders
Roger A. Sayle
Amanda C. Schierz
Steffen Schulze-Kremer
Jack Shirazi
Evangelos Simeonidis
Kieran Smallbone
Larisa N. Soldatova
Andrew Sparkes
Ashwin Srinivasan 0001
Natalie J. Stanford
Michael J. E. Sternberg
Robert Stevens 0001Robert D. Stevens
A. Sutherland
Neil Swainston
Koichi Takahashi
Nada Al taweraqi
Charles C. Taylor
Janet Taylor
Jesper Tegnér
Joshua B. Tenenbaum
Ievgeniia A. Tiukova
Hannu Toivonen
Joaquin Vanschoren
Lovekesh Vig
Yuxuan Wang
Adrian Weller
Ken E. Whelan
Kenneth Whelan
Benjamin J. Whitaker
Christopher K. I. Williams
Paul H. Wise
Michael Young
Mike Young
Hector Zenil
Fang Zhou