Ross D. King
Ross Donald King is a Professor of Machine Intelligence[5] at Chalmers University of Technology.[6]
Ross King | |
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Born | Ross Donald King |
Alma mater |
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Known for | Robot Scientist[1][2] |
Scientific career | |
Fields | |
Institutions | |
Thesis | A machine learning approach to the problem of predicting a protein's secondary structure from its primary structure (PROMIS) (1989) |
Doctoral advisor |
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Website | www |
Education
King completed a Bachelor of Science degree in Microbiology at the University of Aberdeen in 1983 and went on to study for a Master of Science degree in Computer Science at the University of Newcastle in 1985. Following this, he completed a PhD at The Turing Institute at the University of Strathclyde in 1989[3] for work on developing machine learning methods for protein structure prediction.[7]
Research
King's research interests are in the automation of science, drug design, AI, machine learning and synthetic biology.[8][9] He is probably best known for the Robot Scientist[4][10][11][12][13][14][15][16][17] project which has created a robot that can:
- hypothesize to explain observations
- devise experiments to test these hypotheses
- physically run the experiments using laboratory robotics
- interpret the results from the experiments
- repeat the cycle as required
The Robot Scientist can autonomously execute high-throughput hypothesis led research. In addition to automating experimentation Robot Scientists are well suited to recording scientific knowledge: as the experiments are conceived and executed automatically by computer, it is possible to completely capture and digitally curate all aspects of the scientific process. Robot Scientist is the first machine[14][18] to have been demonstrated to have discovered novel scientific knowledge. A new Robot Scientist Eve[19][20][21][22][23] is designed to automate drug discovery. Eve automates high-throughput screening, hit confirmation, and lead generation through QSAR learning and testing. Eve is being applied to the discovery of lead compounds for neglected tropical diseases.
King's research has been funded by the EPSRC[24] and the BBSRC.,[18] European Union, HEFCW, the Royal Academy of Engineering and JISC. He worked at Aberystwyth University for 15 years then moved to Manchester in January 2012. He left the School of Computer Science at the University of Manchester in 2019 and moved to Chalmers University of Technology.
He has an h-index of 54 according to Google Scholar.[25]
Collaborations
In 2000 King was a founder of the spin-out company PharmaDM,[26] which developed data mining tools for the pharmaceutical industry. The company was based largely on research applying data mining to bioinformatics and chemoinformatics. The other scientific founders come from the University of Oxford and Leuven.
King has also developed an algorithm for converting protein coding DNA sequences into music with Colin Angus of The Shamen.[27] The song S2-translation[28] based on this is in the Rough Guide to Rock,[29] and was on an album that sold more than 100,000 copies.
External links
References
- Sparkes, A.; Aubrey, W.; Byrne, E.; Clare, A.; Khan, M. N.; Liakata, M.; Markham, M.; Rowland, J.; Soldatova, L. N.; Whelan, K. E.; Young, M.; King, R. D. (2010). "Towards Robot Scientists for autonomous scientific discovery". Automated Experimentation. 2: 1. doi:10.1186/1759-4499-2-1. PMC 2813846. PMID 20119518.
- King, P.; Rowland, J.; Aubrey, W.; Liakata, M.; Markham, M.; Soldatova, L. N.; Whelan, K. E.; Clare, A.; Young, M.; Sparkes, A.; Oliver, S. G.; Pir, P. (2009). "The Robot Scientist Adam". Computer. 42 (7): 46–54. doi:10.1109/MC.2009.270. S2CID 13920692.
- King, Ross (1989). A machine learning approach to the problem of predicting a protein's secondary structure from its primary structure (PROMIS) (PhD thesis). University of Strathclyde.
- King, Ross D.; Muggleton, Stephen H.; Srinivasan, A.; Sternberg, M. J. (1996). "Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming". Proceedings of the National Academy of Sciences of the United States of America. 93 (1): 438–442. Bibcode:1996PNAS...93..438K. doi:10.1073/pnas.93.1.438. PMC 40253. PMID 8552655.
- "Ross King". Chalmers. 12 December 2017. Retrieved 6 November 2019.
- Srinivasan, A.; Muggleton, S.H.; Sternberg, M.J.E.; King, R.D. (1996). "Theories for mutagenicity: A study in first-order and feature-based induction". Artificial Intelligence. 85 (1–2): 277–299. doi:10.1016/0004-3702(95)00122-0.
- King, R. D.; Sternberg, M. J. E. (1990). "Machine learning approach for the prediction of protein secondary structure". Journal of Molecular Biology. 216 (2): 441–457. doi:10.1016/S0022-2836(05)80333-X. PMID 2254939.
- King, R. D.; Sternberg, M. J. E. (1996). "Identification and application of the concepts important for accurate and reliable protein secondary structure prediction". Protein Science. 5 (11): 2298–2310. doi:10.1002/pro.5560051116. PMC 2143286. PMID 8931148.
- King, R. D.; Muggleton, S.; Lewis, R. A.; Sternberg, M. J. (1992). "Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase". Proceedings of the National Academy of Sciences of the United States of America. 89 (23): 11322–11326. Bibcode:1992PNAS...8911322K. doi:10.1073/pnas.89.23.11322. PMC 50542. PMID 1454814.
- King, R. D.; Liakata, M.; Lu, C.; Oliver, S. G.; Soldatova, L. N. (2011). "On the formalization and reuse of scientific research". Journal of the Royal Society Interface. 8 (63): 1440–1448. doi:10.1098/rsif.2011.0029. PMC 3163424. PMID 21490004.
- Anderson, Philip W.; Abrahams, Elihu (2009). "Machines Fall Short of Revolutionary Science". Science. 324 (5934): 1515–1516. Bibcode:2009Sci...324.1515A. doi:10.1126/science.324_1515c. PMID 19541975.
- Waltz, David; Buchanan, Bruce G. (2009). "Automating Science: Computers with intelligence can design and run experiments, but learning from the results to generate subsequent experiments requires even more intelligence". Science. 324 (5923): 43–44. doi:10.1126/science.1172781. PMID 19342574. S2CID 36543867.
- Stevenson, R. W.; Murphy, J. F.; Clare, T. J. (2009). "Robot Inventors: Patently Impossible?". Science. 324 (5930): 1014. doi:10.1126/science.324_1014a. PMID 19460985.
- King, R. D.; Rowland, J.; Oliver, S. G.; Young, M.; Aubrey, W.; Byrne, E.; Liakata, M.; Markham, M.; Pir, P.; Soldatova, L. N.; Sparkes, A.; Whelan, K. E.; Clare, A. (2009). "Make Way for Robot Scientists". Science. 325 (5943): 945. Bibcode:2009Sci...325R.945K. doi:10.1126/science.325_945a. PMID 19696334.
- King, R. D.; Rowland, J.; Oliver, S. G.; Young, M.; Aubrey, W.; Byrne, E.; Liakata, M.; Markham, M.; Pir, P.; Soldatova, L. N.; Sparkes, A.; Whelan, K. E.; Clare, A. (2009). "The Automation of Science". Science. 324 (5923): 85–89. Bibcode:2009Sci...324...85K. doi:10.1126/science.1165620. PMID 19342587. S2CID 14948753.
- King, R. D.; Whelan, K. E.; Jones, F. M.; Reiser, P. G. K.; Bryant, C. H.; Muggleton, S. H.; Kell, D. B.; Oliver, S. G. (2004). "Functional genomic hypothesis generation and experimentation by a robot scientist". Nature. 427 (6971): 247–252. Bibcode:2004Natur.427..247K. doi:10.1038/nature02236. PMID 14724639. S2CID 4428725.
- King, R. D. (2011). "Rise of the Robo Scientists". Scientific American. 304 (1): 72–76. Bibcode:2011SciAm.304a..72K. doi:10.1038/scientificamerican0111-72. PMID 21265330.
- "2 April 2009 - Robot scientist becomes first machine to discover new scientific knowledge - Media release - BBSRC". Archived from the original on 14 May 2013.
- Wilson, N. (2004). "Technology: A robot scientist". Nature Reviews Genetics. 5 (3): 164. doi:10.1038/nrg1300. S2CID 5633301.
- Bilsland, Elizabeth; Sparkes, Andrew; Williams, Kevin; Moss, Harry J.; de Clare, Michaela; Pir, Pınar; Rowland, Jem; Aubrey, Wayne; Pateman, Ron; Young, Mike; Carrington, Mark; King, Ross D.; Oliver, Stephen G. (2013). "Yeast-based automated high-throughput screens to identify anti-parasitic lead compounds". Open Biology. The Royal Society. 3 (2): 120158. doi:10.1098/rsob.120158. ISSN 2046-2441. PMC 3603448. PMID 23446112.
- Williams, Kevin; Bilsland, Elizabeth; Sparkes, Andrew; Aubrey, Wayne; Young, Michael; Soldatova, Larisa N.; De Grave, Kurt; Ramon, Jan; de Clare, Michaela; Sirawaraporn, Worachart; Oliver, Stephen G.; King, Ross D. (6 March 2015). "Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases". Journal of the Royal Society Interface. The Royal Society. 12 (104): 20141289. doi:10.1098/rsif.2014.1289. ISSN 1742-5689. PMC 4345494. PMID 25652463.
- Bilsland, Elizabeth; van Vliet, Liisa; Williams, Kevin; Feltham, Jack; Carrasco, Marta P.; Fotoran, Wesley L.; Cubillos, Eliana F. G.; Wunderlich, Gerhard; Grøtli, Morten; Hollfelder, Florian; Jackson, Victoria; King, Ross D.; Oliver, Stephen G. (18 January 2018). "Plasmodium dihydrofolate reductase is a second enzyme target for the antimalarial action of triclosan". Scientific Reports. Springer Nature. 8 (1): 1038. Bibcode:2018NatSR...8.1038B. doi:10.1038/s41598-018-19549-x. ISSN 2045-2322. PMC 5773535. PMID 29348637.
- Coutant, Anthony; Roper, Katherine; Trejo-Banos, Daniel; Bouthinon, Dominique; Carpenter, Martin; Grzebyta, Jacek; Santini, Guillaume; Soldano, Henry; Elati, Mohamed; Ramon, Jan; Rouveirol, Celine; Soldatova, Larisa N.; King, Ross D. (16 August 2019). "Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast". Proceedings of the National Academy of Sciences. 116 (36): 18142–18147. doi:10.1073/pnas.1900548116. ISSN 0027-8424. PMC 6731661. PMID 31420515.
- "Grants awarded to Ross King by the Engineering and Physical Sciences Research Council (EPSRC)".
- "Ross D. King". Google Scholar. Retrieved 30 April 2022.
- "Pharmadm.com".
- "Music to my DNA structure". Times Higher Education 2001-05-25. 25 May 2001.
- "The Shamen - S2 Translation (S2 Protein)" on YouTube
- Buckley, Peter J. (2003). The Rough Guide to Rock (Rough Guides). Rough Guides Limited. ISBN 978-1-84353-105-0.