Ross D. King
Ross D. King
Professor of Machine Intelligence, Chalmers University
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Knowledge discovery in multi-label phenotype data
A Clare, RD King
European conference on principles of data mining and knowledge discovery, 42-53, 2001
Functional genomic hypothesis generation and experimentation by a robot scientist
RD King, KE Whelan, FM Jones, PGK Reiser, CH Bryant, SH Muggleton, ...
Nature 427 (6971), 247-252, 2004
The automation of science
RD King, J Rowland, SG Oliver, M Young, W Aubrey, E Byrne, M Liakata, ...
Science 324 (5923), 85-89, 2009
Identification and application of the concepts important for accurate and reliable protein secondary structure prediction
RD King, MJE Sternberg
Protein science 5 (11), 2298-2310, 1996
Statlog: comparison of classification algorithms on large real-world problems
RD King, C Feng, A Sutherland
Applied Artificial Intelligence an International Journal 9 (3), 289-333, 1995
Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops
GS Catchpole, M Beckmann, DP Enot, M Mondhe, B Zywicki, J Taylor, ...
Proceedings of the National Academy of Sciences 102 (40), 14458-14462, 2005
Cascaded multiple classifiers for secondary structure prediction
M Ouali, RD King
Protein Science 9 (6), 1162-1176, 2000
Theories for mutagenicity: A study in first-order and feature-based induction
A Srinivasan, SH Muggleton, MJE Sternberg, RD King
Artificial Intelligence 85 (1-2), 277-299, 1996
Finding Frequent Substructures in Chemical Compounds.
L Dehaspe, H Toivonen, RD King
KDD 98, 1998, 1998
Application of metabolomics to plant genotype discrimination using statistics and machine learning
J Taylor, R King, TA ltmann, O Fiehn
Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
RD King, S Muggleton, RA Lewis, MJ Sternberg
Proceedings of the national academy of sciences 89 (23), 11322-11326, 1992
Protein secondary structure prediction using logic-based machine learning
S Muggleton, RD King, MJE Stenberg
Protein Engineering, Design and Selection 5 (7), 647-657, 1992
Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.
RD King, SH Muggleton, A Srinivasan, MJ Sternberg
Proceedings of the National Academy of Sciences 93 (1), 438-442, 1996
An ontology of scientific experiments
LN Soldatova, RD King
Journal of the royal society interface 3 (11), 795-803, 2006
The predictive toxicology challenge 2000–2001
C Helma, RD King, S Kramer, A Srinivasan
Bioinformatics 17 (1), 107-108, 2001
Mutagenesis: ILP experiments in a non-determinate biological domain
A Srinivasan, S Muggleton, RD King, MJE Sternberg
Proceedings of the 4th international workshop on inductive logic programming …, 1994
Statistical evaluation of the predictive toxicology challenge 2000–2001
H Toivonen, A Srinivasan, RD King, S Kramer, C Helma
Bioinformatics 19 (10), 1183-1193, 2003
Active learning for regression based on query by committee
R Burbidge, JJ Rowland, RD King
Intelligent Data Engineering and Automated Learning-IDEAL 2007: 8th …, 2007
Towards robot scientists for autonomous scientific discovery
A Sparkes, W Aubrey, E Byrne, A Clare, MN Khan, M Liakata, M Markham, ...
Automated experimentation 2, 1-11, 2010
Predicting gene function in Saccharomyces cerevisiae
A Clare, RD King
Bioinformatics-Oxford 19 (2), 42-49, 2003
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