2026 Ross D. King: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	53	4759	4625	283	281	197	13340

Overview

Ross D. King is affiliated with the University of Manchester in the United Kingdom. Their research spans the intersection of biochemistry, genetics, molecular biology, and computer science, with significant contributions in subfields such as molecular biology, artificial intelligence, computational theory and mathematics, materials chemistry, and signal processing.

The scientist's work covers a range of main topics including:

Computational Drug Discovery Methods
Bioinformatics and Genomic Networks
Machine Learning in Materials Science
Microbial Metabolic Engineering and Bioproduction
Machine Learning and Data Classification
Biomedical Text Mining and Ontologies
Metabolomics and Mass Spectrometry Studies

Ross D. King has published regularly in venues such as:

arXiv (Cornell University)
bioRxiv (Cold Spring Harbor Laboratory)
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning
Proceedings of the National Academy of Sciences

Some recent papers include:

"Cross-validation is safe to use," 2021, Nature Machine Intelligence
"Transformational machine learning: Learning how to learn from many related scientific problems," 2021, Proceedings of the National Academy of Sciences
"Batched Bayesian Optimization for Drug Design in Noisy Environments," 2022, Journal of Chemical Information and Modeling
"Improved prediction of gene expression through integrating cell signalling models with machine learning," 2022, BMC Bioinformatics
"A Genetic Trap in Yeast for Inhibitors of SARS-CoV-2 Main Protease," 2021, mSystems

Frequent collaborators with Ross D. King include:

Ievgeniia Tiukova
Oghenejokpeme I. Orhobor
Larisa Soldatova
Daniel Brunnsåker
Alexander H. Gower

Best Publications

Knowledge Discovery in Multi-label Phenotype Data

Amanda Clare;Ross D. King

996
Citations
Functional genomic hypothesis generation and experimentation by a robot scientist

Ross D. King;Kenneth E. Whelan;Ffion M. Jones;Philip G. K. Reiser

847
Citations
The Automation of Science

Ross Donald King;Jeremy John Rowland;Jeremy John Rowland;Stephen G. Oliver;Stephen G. Oliver;Michael Young

779
Citations
Identification and application of the concepts important for accurate and reliable protein secondary structure prediction

Ross D. King;Michael J.E. Sternberg

666
Citations
Finding frequent substructures in chemical compounds

Luc Dehaspe;H Toivonen;Ross D King

637
Citations
STATLOG: COMPARISON OF CLASSIFICATION ALGORITHMS ON LARGE REAL-WORLD PROBLEMS

Ross D. King;Cao Feng;A. Sutherland

457
Citations
Cascaded multiple classifiers for secondary structure prediction.

Mohammed Ouali;Ross D. King

455
Citations
Theories for mutagenicity: a study in first-order and feature-based induction

Ashwin Srinivasan;S. H. Muggleton;M. J. E. Sternberg;R. D. King

454
Citations
Application of metabolomics to plant genotype discrimination using statistics and machine learning

Janet Taylor;Ross Donald King;Thomas Altmann;Oliver Fiehn

417
Citations
Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.

Ross D. King;Stephen Muggleton;Richard A. Lewis;Michael J. E. Sternberg

387
Citations
Protein secondary structure prediction using logic-based machine learning

S. Muggleton;R.D. King;M.J.E. Sternberg

386
Citations
Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

Ross D. King;Stephen H. Muggleton;Ashwin Srinivasan;Michael J. E. Sternberg

354
Citations
An ontology of scientific experiments

Larisa N Soldatova;Ross D King

298
Citations
The predictive toxicology challenge 2000-2001

Christoph Helma;Ross D. King;Stefan Kramer;Ashwin Srinivasan

275
Citations
Statistical evaluation of the Predictive Toxicology Challenge 2000-2001.

Hannu Toivonen;Ashwin Srinivasan;Ross D. King;Stefan Kramer

217
Citations
Active learning for regression based on query by committee

Robert Burbidge;Jem J. Rowland;Ross D. King

196
Citations
Towards Robot Scientists for autonomous scientific discovery

Andrew Charles Sparkes;Wayne Aubrey;Emma Louise Byrne;Amanda Janet Clare

194
Citations
Carcinogenesis Predictions Using ILP

Ashwin Srinivasan;Ross D. King;Stephen Muggleton;Michael J. E. Sternberg

188
Citations
Predicting gene function in Saccharomyces cerevisiae.

Amanda Clare;Ross D King

186
Citations
Relating chemical activity to structure: An examination of ILP successes

Ross D. King;Michael J. E. Sternberg;Ashwin Srinivasan

168
Citations

Frequent Co-Authors

Michael J.E. Sternberg Imperial College London

Stephen Muggleton Imperial College London

Stephen G. Oliver University of Cambridge

Maria Liakata Queen Mary University of London

Douglas B. Kell University of Liverpool

Crina Grosan King's College London

Hannu Toivonen University of Helsinki

David P. Enot Institut Gustave Roussy

Joaquin Vanschoren Eindhoven University of Technology

Pedro Mendes University of Connecticut

External Links

Wikipedia of Ross D. King Personal website of Ross D. King Google Scholar page

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