An Inductive Learning Approach to the Problem of Predicting a Protein's Secondary Structure from Its Amino Acid Sequence. - Ross D. King (EWSL, 1987)

A machine learning approach to the problem of predicting a protein's secondary structure from its primary structure - Ross D. King (1988)

IPSA: Inductive Protein Structure Analysis. - Steffen Schulze-Kremer; Ross D. King (EWSL, 1991)

Statistical Methods in Learning. - A. Sutherland; Bob Henery; Rafael Molina 001; Charles C. Taylor; Ross D. King (IPMU, 1992)

A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Network. - Ross D. King; Bob Henery; Cao Feng; A. Sutherland (Machine Intelligence 13, 1992)

Machine Learning and biomolecular modelling. - Michael J. E. Sternberg; R. A. Lewis; Ross D. King; Stephen H. Muggleton (Machine Intelligence 13, 1992)

Discovery of Protein Structural Constraints in a Deductive Database using Inductive Logic Programming. - Ross D. King; Dominic A. Clark; Jack Shirazi; Michael J. E. Sternberg (Machine Intelligence 14, 1993)

Inductive Logic Programming Used to Discover Topological Constraints in Protein Structures. - Ross D. King; Dominic A. Clark; Jack Shirazi; Michael J. E. Sternberg (ISMB, 1994)

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines. - Jonathan D. Hirst; Ross D. King; Michael J. E. Sternberg (J. Comput. Aided Mol. Des., 1994)

Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines. - Jonathan D. Hirst; Ross D. King; Michael J. E. Sternberg (J. Comput. Aided Mol. Des., 1994)

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. - Ross D. King (Knowl. Eng. Rev., 1994)

Applications of Inductive Logic Programming. - Ivan Bratko; Ross D. King (SIGART Bull., 1994)

Drug Design by Machine Learning. - Michael J. E. Sternberg; Ross D. King; Ashwin Srinivasan 001; Stephen H. Muggleton (Machine Intelligence 15, 1995)

Comparison of artificial intelligence methods for modeling pharmaceutical QSARS. - Ross D. King (Appl. Artif. Intell., 1995)

STALOG: Comparison of classification algorithms on large real-world problems. - Ross D. King; Cao Feng; A. Sutherland (Appl. Artif. Intell., 1995)

Relating Chemical Activity to Structure: An Examination of ILP Successes. - Ross D. King; Michael J. E. Sternberg; Ashwin Srinivasan 001 (New Gener. Comput., 1995)

Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity by Structural Attributes. - Ashwin Srinivasan 001; Ross D. King (Inductive Logic Programming Workshop, 1996)

Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction. - Ashwin Srinivasan 001; Stephen H. Muggleton; Michael J. E. Sternberg; Ross D. King (Artif. Intell., 1996)

PM - protein music. - Ross D. King; C. G. Angus (Comput. Appl. Biosci., 1996)

The Predictive Toxicology Evaluation Challenge. - Ashwin Srinivasan 001; Ross D. King; Stephen H. Muggleton; Michael J. E. Sternberg (IJCAI (1), 1997)

Carcinogenesis Predictions Using ILP. - Ashwin Srinivasan 001; Ross D. King; Stephen H. Muggleton; Michael J. E. Sternberg (ILP, 1997)

DSC: public domain protein secondary structure predication. - Ross D. King; Mansoor A. S. Saqi; Roger A. Sayle; Michael J. E. Sternberg (Comput. Appl. Biosci., 1997)

The discovery of indicator variables for QSAR using inductive logic programming. - Ross D. King; Ashwin Srinivasan 001 (J. Comput. Aided Mol. Des., 1997)

Biochemical Knowledge Discovery Using Inductive Logic Programming. - Stephen H. Muggleton; Ashwin Srinivasan 001; Ross D. King; Michael J. E. Sternberg (Discovery Science, 1998)

Finding Frequent Substructures in Chemical Compounds. - Luc Dehaspe; Hannu Toivonen; Ross D. King (KDD, 1998)

An assessment of submissions made to the Predictive Toxicology Evaluation Challenge. - Ashwin Srinivasan 001; Ross D. King; Douglas W. Bristol (IJCAI, 1999)

An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment. - Ashwin Srinivasan 001; Ross D. King; Douglas W. Bristol (ILP, 1999)

Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes. - Ashwin Srinivasan 001; Ross D. King (Data Min. Knowl. Discov., 1999)

Genome scale prediction of protein functional class from sequence using data mining. - Ross D. King; Andreas Karwath; Amanda Clare; Luc Dehaspe (KDD, 2000)

An Automated ILP Server in the Field of Bioinformatics. - Andreas Karwath; Ross D. King (ILP, 2001)

Knowledge Discovery in Multi-label Phenotype Data. - Amanda Clare; Ross D. King (PKDD, 2001)

The Predictive Toxicology Challenge 2000-2001. - Christoph Helma; Ross D. King; Stefan Kramer 001; Ashwin Srinivasan 001 (Bioinform., 2001)

The utility of different representations of protein sequence for predicting functional class. - Ross D. King; Andreas Karwath; Amanda Clare; Luc Dehaspe (Bioinform., 2001)

Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes. - Christopher H. Bryant; Stephen H. Muggleton; Stephen G. Oliver; Douglas B. Kell; Philip G. K. Reiser; Ross D. King (Electron. Trans. Artif. Intell., 2001)

A quantum mechanics based representation of molecules for machine inference. - Ross D. King; Nathalie Marchand-Geneste; Bjørn K. Alsberg (Electron. Trans. Artif. Intell., 2001)

Developing a Logical Model of Yeast Metabolism. - Philip G. K. Reiser; Ross D. King; Douglas B. Kell; Stephen H. Muggleton; Christopher H. Bryant; Stephen G. Oliver (Electron. Trans. Artif. Intell., 2001)

Warmr: a data mining tool for chemical data. - Ross D. King; Ashwin Srinivasan 001; Luc Dehaspe (J. Comput. Aided Mol. Des., 2001)

Application of metabolomics to plant genotype discrimination using statistics and machine learning. - Janet Taylor; Ross D. King; Thomas Altmann; Oliver Fiehn (ECCB, 2002)

Machine learning of functional class from phenotype data. - Amanda Clare; Ross D. King (Bioinform., 2002)

Homology Induction: the use of machine learning to improve sequence similarity searches. - Andreas Karwath; Ross D. King (BMC Bioinform., 2002)

How well do we understand the clusters found in microarray data? - Amanda Clare; Ross D. King (Silico Biol., 2002)

Predicting gene function in Saccharomyces cerevisiae. - Amanda Clare; Ross D. King (ECCB, 2003)

A Personal View of How Best to Apply ILP. - Ross D. King (ILP, 2003)

Data Mining the Yeast Genome in a Lazy Functional Language. - Amanda Clare; Ross D. King (PADL, 2003)

Application of Inductive Logic Programming to Structure-Based Drug Design. - David P. Enot; Ross D. King (PKDD, 2003)

Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001. - Hannu Toivonen; Ashwin Srinivasan 001; Ross D. King; Stefan Kramer 001; Christoph Helma (Bioinform., 2003)

An Empirical Study of the Use of Relevance Information in Inductive Logic Programming. - Ashwin Srinivasan 001; Ross D. King; Michael Bain 001 (J. Mach. Learn. Res., 2003)

Learning Qualitative Metabolic Models. - George Macleod Coghill; Simon M. Garrett; Ross D. King (ECAI, 2004)

BLID: An Application of Logical Information Systems to Bioinformatics. - Sébastien Ferré; Ross D. King (ICFCA, 2004)

Poly-transformation. - Ross D. King; Mohammed Ouali (IDEAL, 2004)

Applying Inductive Logic Programming to Predicting Gene Function. - Ross D. King (AI Mag., 2004)

Confirmation of data mining based predictions of protein function. - Ross D. King; Paul H. Wise; Amanda Clare (Bioinform., 2004)

The Robot Scientist Project. - Ross D. King (ALT, 2005)

The Robot Scientist Project. - Ross D. King; Michael Young; Amanda Clare; Kenneth Whelan; Jem J. Rowland (Discovery Science, 2005)

On the use of qualitative reasoning to simulate and identify metabolic pathway. - Ross D. King; Simon M. Garrett; George Macleod Coghill (Bioinform., 2005)

A Dichotomic Search Algorithm for Mining and Learning in Domain-Specific Logics. - Sébastien Ferré; Ross D. King (Fundam. Informaticae, 2005)

An ontology for a Robot Scientist. - Larisa N. Soldatova; Amanda Clare; Andrew Sparkes; Ross D. King (ISMB (Supplement of Bioinformatics), 2006)

Functional bioinformatics for Arabidopsis thaliana. - Amanda Clare; Andreas Karwath; Helen Ougham; Ross D. King (Bioinform., 2006)

Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships. - Bård Buttingsrud; Einar Ryeng; Ross D. King; Bjørn K. Alsberg (J. Comput. Aided Mol. Des., 2006)

Finding Motifs in Protein Secondary Structure for Use in Function Prediction. - Sébastien Ferré; Ross D. King (J. Comput. Biol., 2006)

Guest editorial. - Rui Camacho; Ross D. King; Ashwin Srinivasan 001 (Mach. Learn., 2006)

Quantitative pharmacophore models with inductive logic programming. - Ashwin Srinivasan 001; David Page; Rui Camacho; Ross D. King (Mach. Learn., 2006)

Evolutionary Optimization of Three-Photon Absorption in Molecular Iodine. - Robert Burbidge; Jem J. Rowland; Ross D. King; Nicholas T. Form; Benjamin J. Whitaker (CIDM, 2007)

Learning Qualitative Models of Physical and Biological Systems. - Simon M. Garrett; George Macleod Coghill; Ashwin Srinivasan 001; Ross D. King (Computational Discovery of Scientific Knowledge, 2007)

Logic and the Automatic Acquisition of Scientific Knowledge: An Application to Functional Genomics. - Ross D. King; Andreas Karwath; Amanda Clare; Luc Dehaspe (Computational Discovery of Scientific Knowledge, 2007)

Active Learning for Regression Based on Query by Committee. - Robert Burbidge; Jem J. Rowland; Ross D. King (IDEAL, 2007)

Locational distribution of gene functional classes in Arabidopsis thaliana. - Michael C. Riley; Amanda Clare; Ross D. King (BMC Bioinform., 2007)

Formalising Phylogenetic Experiments: Ontologies and Logical Inference. - Ross D. King; Larisa N. Soldatova (AAAI Spring Symposium: Symbiotic Relationships between Semantic Web and Knowledge Engineering, 2008)

The EXACT description of biomedical protocols. - Larisa N. Soldatova; Wayne Aubrey; Ross D. King; Amanda Clare (ISMB, 2008)

Using a logical model to predict the growth of yeast. - Ken E. Whelan; Ross D. King (BMC Bioinform., 2008)

Qualitative System Identification from Imperfect Data. - George Macleod Coghill; Ashwin Srinivasan 001; Ross D. King (J. Artif. Intell. Res., 2008)

Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming. - Ashwin Srinivasan 001; Ross D. King (J. Mach. Learn. Res., 2008)

A Nonmonotonic Logical Approach for Modelling and Revising Metabolic Networks. - Oliver Ray; Ken E. Whelan; Ross D. King (CISIS, 2009)

Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data. - Oliver Ray; Ken E. Whelan; Ross D. King (ILP, 2009)

Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds. - Amanda C. Schierz; Ross D. King (PRIB, 2009)

An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems. - Chuan Lu; Ross D. King (Bioinform., 2009)

The Robot Scientist Adam. - 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 (Computer, 2009)

Inductive Queries for a Drug Designing Robot Scientist. - Ross D. King; Amanda C. Schierz; Amanda Clare; Jem J. Rowland; Andrew Sparkes; Siegfried Nijssen; Jan Ramon (Inductive Databases and Constraint-Based Data Mining, 2010)

The Analysis of Yeast Cell Morphology Using a Robot Scientist. - Yihui Liu; Katherine Martin; Andrew Sparkes; Ross D. King (CIS, 2010)

Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition. - Oliver Ray; Ken E. Whelan; Ross D. King (CISIS, 2010)

Further developments towards a genome-scale metabolic model of yeast. - 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; et al. (BMC Syst. Biol., 2010)

An Ontology for Description of Drug Discovery Investigations. - Da Qi; Ross D. King; Andrew L. Hopkins; G. Richard J. Bickerton; Larisa N. Soldatova (J. Integr. Bioinform., 2010)

Numbers as Data Structures: The Prime Successor Function as Primitive - Ross D. King (CoRR, 2011)

Topic Models with Relational Features for Drug Design. - Tanveer A. Faruquie; Ashwin Srinivasan 001; Ross D. King (ILP, 2012)

Representation of probabilistic scientific knowledge. - Larisa N. Soldatova; Andrey Rzhetsky; Kurt De Grave; Ross D. King (J. Biomed. Semant., 2013)

Predicting the Geographical Origin of Music. - Fang Zhou; Claire Q; Ross D. King (ICDM, 2014)

EXACT2: the semantics of biomedical protocols. - Larisa N. Soldatova; Daniel Nadis; Ross D. King; Piyali S. Basu; Emma Haddi; Véronique Baumlé; Nigel J. Saunders; Wolfgang Marwan; Brian B. Rudkin (BMC Bioinform., 2014)

An investigation into eukaryotic pseudouridine synthases. - Ross D. King; Chuan Lu (J. Bioinform. Comput. Biol., 2014)

Automating the Development of Metabolic Network Models. - Robert Rozanski; Stefano Bragaglia; Oliver Ray; Ross D. King (CMSB, 2015)

Meta-QSAR: Learning How to Learn QSARs. - Iván Olier; Crina Grosan; Noureddin Sadawi; Larisa N. Soldatova; Ross D. King (MetaSel@PKDD/ECML, 2015)

Computing exponentially faster: Implementing a nondeterministic universal Turing machine using DNA. - Andrew Currin; Konstantin Korovin; Maria Ababi; Katherine Roper; Douglas B. Kell; Philip J. Day; Ross D. King (CoRR, 2016)

On the Use of Computer Programs as Money. - Ross D. King (CoRR, 2016)

Towards the Machine Reading of Arabic Calligraphy: A Letters Dataset and Corresponding Corpus of Text. - Seetah ALSalamah; Ross D. King (ASAR, 2018)

Predicting Rice Phenotypes with Meta-learning. - Oghenejokpeme I. Orhobor; Nickolai N. Alexandrov; Ross D. King (DS, 2018)

Large-Scale Assessment of Deep Relational Machines. - Tirtharaj Dash; Ashwin Srinivasan 001; Lovekesh Vig; Oghenejokpeme I. Orhobor; Ross D. King (ILP, 2018)

Transformative Machine Learning. - Iván Olier; Oghenejokpeme I. Orhobor; Joaquin Vanschoren; Ross D. King (CoRR, 2018)

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. - Iván Olier; Noureddin Sadawi; G. Richard J. Bickerton; Joaquin Vanschoren; Crina Grosan; Larisa N. Soldatova; Ross D. King (Mach. Learn., 2018)

Automating Sciences: Philosophical and Social Dimensions. - Ross D. King; Vlad Schuler Costa; Chris Mellingwood; Larisa N. Soldatova (IEEE Technol. Soc. Mag., 2018)

Using Prior Knowledge to Facilitate Computational Reading of Arabic Calligraphy. - Seetah ALSalamah; Riza Batista-Navarro; Ross D. King (IDEAL (2), 2019)

Multi-task learning with a natural metric for quantitative structure activity relationship learning. - Noureddin Sadawi; Iván Olier; Joaquin Vanschoren; Jan N. van Rijn; Jeremy Besnard; G. Richard J. Bickerton; Crina Grosan; Larisa N. Soldatova; Ross D. King (J. Cheminformatics, 2019)

Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology. - Oghenejokpeme I. Orhobor; Joseph French; Larisa N. Soldatova; Ross D. King (DS, 2020)

Federated Ensemble Regression Using Classification. - Oghenejokpeme I. Orhobor; Larisa N. Soldatova; Ross D. King (DS, 2020)

Self-supervised learning of object slippage: An LSTM model trained on low-cost tactile sensors. - Ainur Begalinova; Ross D. King; Barry Lennox; Riza Batista-Navarro (IRC, 2020)

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat. - Nastasiya F. Grinberg; Oghenejokpeme I. Orhobor; Ross D. King (Mach. Learn., 2020)

Predicting rice phenotypes with meta and multi-target learning. - Oghenejokpeme I. Orhobor; Nickolai N. Alexandrov; Ross D. King (Mach. Learn., 2020)

Cross-validation is safe to use. - Ross D. King; Oghenejokpeme I. Orhobor; Charles C. Taylor (Nat. Mach. Intell., 2021)

Improved prediction of gene expression through integrating cell signalling models with machine learning. - Nada Al taweraqi; Ross D. King (BMC Bioinform., 2022)

Batched Bayesian Optimization for Drug Design in Noisy Environments. - Hugo Bellamy; Abbi Abdel-Rehim; Oghenejokpeme I. Orhobor; Ross D. King (J. Chem. Inf. Model., 2022)

LGEM+: A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction. - Alexander H. Gower; Konstantin Korovin; Daniel Brunnsåker; Ievgeniia A. Tiukova; Ross D. King (DS, 2023)

RIMBO - An Ontology for Model Revision Databases. - Filip Kronström; Alexander H. Gower; Ievgeniia A. Tiukova; Ross D. King (DS, 2023)

Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction. - Abbi Abdel-Rehim; Oghenejokpeme I. Orhobor; Hang Lou; Hao Ni; Ross D. King (CoRR, 2023)

Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems. - Stefan Kramer 001; Mattia Cerrato; Saso Dzeroski; Ross D. King (CoRR, 2023)

The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence. - Hector Zenil; Jesper Tegnér; Felipe S. Abrahão; Alexander Lavin; Vipin Kumar 001; Jeremy G. Frey; Adrian Weller; Larisa N. Soldatova; Alan R. Bundy; Nicholas R. Jennings; Koichi Takahashi; Lawrence Hunter; et al. (CoRR, 2023)

Extension of Transformational Machine Learning: Classification Problems. - Adnan Mahmud; Oghenejokpeme I. Orhobor; Ross D. King (CoRR, 2023)

Imbalanced regression using regressor-classifier ensembles. - Oghenejokpeme I. Orhobor; Nastasiya F. Grinberg; Larisa N. Soldatova; Ross D. King (Mach. Learn., 2023)

Protein-ligand binding affinity prediction exploiting sequence constituent homology. - Abbi Abdel-Rehim; Oghenejokpeme I. Orhobor; Hang Lou; Hao Ni; Ross D. King (Bioinform., 2023)

Random Forests for Heteroscedastic Data. - Hugo Bellamy; Ross D. King (DS (2), 2024)

Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment. - Abbi Abdel-Rehim; Hector Zenil; Oghenejokpeme I. Orhobor; Marie Fisher; Ross J. Collins; Elizabeth Bourne; Gareth W. Fearnley; Emma Tate; Holly X. Smith; Larisa N. Soldatova; Ross D. King (CoRR, 2024)

The Use of AI-Robotic Systems for Scientific Discovery. - Alexander H. Gower; Konstantin Korovin; Daniel Brunnsåker; Filip Kronström; Gabriel K. Reder; Ievgeniia A. Tiukova; Ronald S. Reiserer; John P. Wikswo; Ross D. King (CoRR, 2024)

Genesis: Towards the Automation of Systems Biology Research. - Ievgeniia A. Tiukova; Daniel Brunnsåker; Erik Y. Bjurström; Alexander H. Gower; Filip Kronström; Gabriel K. Reder; Ronald S. Reiserer; Konstantin Korovin; Larisa N. Soldatova; John P. Wikswo; Ross D. King (CoRR, 2024)

Personalised Medicine: Establishing predictive machine learning models for drug responses in patient derived cell culture. - Abbi Abdel-Rehim; Oghenejokpeme I. Orhobor; Gareth Griffiths 006; Larisa N. Soldatova; Ross D. King (CoRR, 2024)

Automating the Practice of Science - Opportunities, Challenges, and Implications. - Sebastian Musslick; Laura Bartlett; Suyog H. Chandramouli; Marina Dubova; Fernand Gobet; Thomas L. Griffiths 001; Jessica Hullman; Ross D. King; J. Nathan Kutz; Christopher G. Lucas; Suhas Mahesh; Franco Pestilli; et al. (CoRR, 2024)

Extrapolation is not the same as interpolation. - Yuxuan Wang; Ross D. King (Mach. Learn., 2024)

Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data. - Hugo Bellamy; Joachim Dickhaut; Ross D. King (J. Chem. Inf. Model., 2025)

Learning Logical Rules using Minimum Message Length. - Ruben Sharma; Sebastijan Dumancic; Ross D. King; Andrew Cropper (CoRR, 2025)