Ross King

Full Professor at Data Science and AI

Ross King is Professor of Machine Intelligence.

Source: chalmers.se
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Showing 22 publications

2024

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Gabriel Reder, Erik Bjurström, Daniel Brunnsåker et al
Journal of the American Society for Mass Spectrometry. Vol. 35 (3), p. 542-550
Journal article
2024

Interpreting protein abundance in Saccharomyces cerevisiae through relational learning

Daniel Brunnsåker, Filip Kronström, Ievgeniia Tiukova et al
Bioinformatics. Vol. 40 (2)
Journal article
2023

Imbalanced regression using regressor-classifier ensembles

Oghenejokpeme I. Orhobor, Nastasiya F. Grinberg, Larisa N. Soldatova et al
Machine Learning. Vol. 112 (4), p. 1365-1387
Journal article
2023

LGEM+: A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction

Alexander Gower, Konstantin Korovin, Daniel Brunnsåker et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14276 LNAI, p. 628-643
Paper in proceeding
2023

Extrapolation is Not the Same as Interpolation

Yuxuan Wang, Ross King
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14276 LNAI, p. 277-292
Paper in proceeding
2023

High-throughput metabolomics for the design and validation of a diauxic shift model

Daniel Brunnsåker, Gabriel Reder, Nikulkumar Soni et al
NPJ systems biology and applications. Vol. 9 (1), p. 11-
Journal article
2023

The automated AI-driven future of scientific discovery

Hector Zenil, Ross King
Artificial Intelligence For Science: A Deep Learning Revolution, p. 679-691
Book chapter
2023

Protein-ligand binding affinity prediction exploiting sequence constituent homology

Abbi Abdel-Rehim, Oghenejokpeme I. Orhobor, Lou Hang et al
Bioinformatics. Vol. 39 (8)
Journal article
2023

Genesis-DB: a database for autonomous laboratory systems

Gabriel Reder, Alexander Gower, Filip Kronström et al
Bioinformatics Advances. Vol. 3 (1)
Journal article
2023

RIMBO - An Ontology for Model Revision Databases

Filip Kronström, Alexander Gower, Ievgeniia Tiukova et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 14276 LNAI, p. 523-534
Paper in proceeding
2022

Improved prediction of gene expression through integrating cell signalling models with machine learning

Nada Al taweraqi, Ross King
BMC Bioinformatics. Vol. 23 (1)
Journal article
2022

Testing the reproducibility and robustness of the cancer biology literature by robot

Katherine Roper, A. Abdel-Rehim, Sonya Hubbard et al
Journal of the Royal Society Interface. Vol. 19 (189)
Journal article
2022

A simple spatial extension to the extended connectivity interaction features for binding affinity prediction

Oghenejokpeme I. Orhobor, Abbi Abdel Rehim, Hang Lou et al
Royal Society Open Science. Vol. 9 (5)
Journal article
2021

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding

Kanix Wang, Robert Stevens, Halima Alachram et al
npj Systems Biology and Applications. Vol. 7 (1)
Journal article
2021

Cross-validation is safe to use

Ross King, Oghenejokpeme I. Orhobor, Charles C. Taylor
Nature Machine Intelligence. Vol. 3 (4), p. 276-276
Other text in scientific journal
2021

Transformational machine learning: Learning how to learn from many related scientific problems

Ivan Olier, Oghenejokpeme I. Orhobor, Tirtharaj Dash et al
Proceedings of the National Academy of Sciences of the United States of America. Vol. 118 (49)
Journal article
2021

A Genetic Trap in Yeast for Inhibitors of SARS-CoV-2 Main Protease

Hanna Alalam, Sunniva Sigurdardottir, Catarina Bourgard et al
MSYSTEMS. Vol. 6 (6)
Journal article
2020

Federated Ensemble Regression Using Classification

Oghenejokpeme I. Orhobor, Larisa N. Soldatova, Ross King
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 12323 LNAI, p. 325-339
Paper in proceeding
2020

Predicting rice phenotypes with meta and multi-target learning

Oghenejokpeme I. Orhobor, Nickolai N. Alexandrov, Ross King
Machine Learning. Vol. 109 (11), p. 2195-2212
Journal article
2020

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

Nastasiya F. Grinberg, Oghenejokpeme I. Orhobor, Ross King
Machine Learning. Vol. 109 (2), p. 251-277
Journal article
2020

Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology

Oghenejokpeme I. Orhobor, Joseph French, Larisa N. Soldatova et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 12323 LNAI, p. 374-385
Paper in proceeding
2020

Self-supervised learning of object slippage: An LSTM model trained on low-cost tactile sensors

Ainur Begalinova, Ross King, Barry Lennox et al
Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020, p. 191-196
Paper in proceeding

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Showing 1 research projects

2021–2024

Closed-loop inlärning av genome-scale metaboliska modeller med hjälp av "Robot Forskaren" Genesis

Ross King Systems and Synthetic Biology
Swedish Research Council (VR)

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