Ross King

Full Professor at Systems and Synthetic Biology

Ross King is Professor of Machine Intelligence.

Source: chalmers.se
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Showing 11 publications

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

Imbalanced regression using regressor-classifier ensembles

Oghenejokpeme I. Orhobor, Nastasiya F. Grinberg, Larisa N. Soldatova et al
Machine Learning. Vol. In Press
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

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
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

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
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
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

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

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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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