Journal ArticleDOI
Functional genomic hypothesis generation and experimentation by a robot scientist
Ross D. King,Kenneth E. Whelan,Ffion M. Jones,Philip G. K. Reiser,Christopher H. Bryant,Stephen Muggleton,Douglas B. Kell,Stephen G. Oliver +7 more
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TLDR
A physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation and shows that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold, both cheapest and random-experiment selection.Abstract:
The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.read more
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Machine learning
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Distilling Free-Form Natural Laws from Experimental Data
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Supporting Online Material for Distilling Free-Form Natural Laws from Experimental Data
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TL;DR: This work proposes a principle for the identification of nontriviality, and demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula, and discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation.
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Probabilistic machine learning and artificial intelligence
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References
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Journal ArticleDOI
KEGG: Kyoto Encyclopedia of Genes and Genomes
Minoru Kanehisa,Susumu Goto +1 more
TL;DR: The Kyoto Encyclopedia of Genes and Genomes (KEGG) as discussed by the authors is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Functional profiling of the Saccharomyces cerevisiae genome.
Guri Giaever,Angela M. Chu,Li Ni,Carla Connelly,Linda Riles,Steeve Veronneau,Sally Dow,Ankuta Lucau-Danila,Keith Anderson,Bruno André,Adam P. Arkin,Anna Astromoff,Mohamed El Bakkoury,Rhonda Bangham,Rocío Benito,Sophie Brachat,Stefano Campanaro,Matt Curtiss,Karen Davis,Adam M. Deutschbauer,K. D. Entian,Patrick Flaherty,Françoise Foury,David J. Garfinkel,Mark Gerstein,Deanna Gotte,Ulrich Güldener,Johannes H. Hegemann,Svenja Hempel,Zelek S. Herman,Daniel F. Jaramillo,Diane E. Kelly,Steven L. Kelly,Peter Kötter,Darlene LaBonte,David C. Lamb,Ning Lan,Hong Liang,Hong Liao,Lucy Y. Liu,Chuanyun Luo,Marc Lussier,Rong Mao,Patrice Menard,Siew Loon Ooi,José L. Revuelta,Christopher J. Roberts,Matthias Rose,Petra Ross-Macdonald,Bart Scherens,Greg Schimmack,Brenda Shafer,Daniel D. Shoemaker,Sharon Sookhai-Mahadeo,Reginald Storms,Jeffrey N. Strathern,Giorgio Valle,Marleen Voet,Guido Volckaert,Ching Yun Wang,Teresa R. Ward,Julie Wilhelmy,Elizabeth A. Winzeler,Yonghong Yang,Grace Yen,Elaine M. Youngman,Kexin Yu,Howard Bussey,Jef D. Boeke,Michael Snyder,Peter Philippsen,Ronald W. Davis,Mark Johnston +72 more
TL;DR: It is shown that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment, and less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal Growth in four of the tested conditions.