6 results on '"Clare, Amanda"'
Search Results
2. The automation of science.
- Author
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King RD, Rowland J, Oliver SG, Young M, Aubrey W, Byrne E, Liakata M, Markham M, Pir P, Soldatova LN, Sparkes A, Whelan KE, and Clare A
- Subjects
- Computers, Genomics, Programming Languages, Robotics, Saccharomyces cerevisiae enzymology, Saccharomyces cerevisiae growth & development, Saccharomyces cerevisiae metabolism, Software, Artificial Intelligence, Automation, Computational Biology, Enzymes genetics, Genes, Fungal, Saccharomyces cerevisiae genetics
- Abstract
The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.
- Published
- 2009
- Full Text
- View/download PDF
3. An ontology for a Robot Scientist.
- Author
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Soldatova LN, Clare A, Sparkes A, and King RD
- Subjects
- Cell Culture Techniques instrumentation, Cell Culture Techniques methods, Natural Language Processing, Research instrumentation, Research Design, Robotics instrumentation, Saccharomyces cerevisiae Proteins physiology, Science instrumentation, Science methods, Vocabulary, Controlled, Artificial Intelligence, Database Management Systems, Databases, Factual, Documentation methods, Information Storage and Retrieval methods, Robotics methods, Saccharomyces cerevisiae physiology
- Abstract
Motivation: A Robot Scientist is a physically implemented robotic system that can automatically carry out cycles of scientific experimentation. We are commissioning a new Robot Scientist designed to investigate gene function in S. cerevisiae. This Robot Scientist will be capable of initiating >1,000 experiments, and making >200,000 observations a day. Robot Scientists provide a unique test bed for the development of methodologies for the curation and annotation of scientific experiments: because the experiments are conceived and executed automatically by computer, it is possible to completely capture and digitally curate all aspects of the scientific process. This new ability brings with it significant technical challenges. To meet these we apply an ontology driven approach to the representation of all the Robot Scientist's data and metadata., Results: We demonstrate the utility of developing an ontology for our new Robot Scientist. This ontology is based on a general ontology of experiments. The ontology aids the curation and annotating of the experimental data and metadata, and the equipment metadata, and supports the design of database systems to hold the data and metadata., Availability: EXPO in XML and OWL formats is at: http://sourceforge.net/projects/expo/. All materials about the Robot Scientist project are available at: http://www.aber.ac.uk/compsci/Research/bio/robotsci/.
- Published
- 2006
- Full Text
- View/download PDF
4. Machine learning of functional class from phenotype data.
- Author
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Clare A and King RD
- Subjects
- Computational Biology, Databases, Genetic, Mutation, Open Reading Frames, Saccharomyces cerevisiae genetics, Artificial Intelligence, Phenotype
- Abstract
Motivation: Mutant phenotype growth experiments are an important novel source of functional genomics data which have received little attention in bioinformatics. We applied supervised machine learning to the problem of using phenotype data to predict the functional class of Open Reading Frames (ORFs) in Saccaromyces cerevisiae. Three sources of data were used: TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces (TRIPLES), European Functional Analysis Network (EUROFAN) and Munich Information Center for Protein Sequences (MIPS). The analysis of the data presented a number of challenges to machine learning: multi-class labels, a large number of sparsely populated classes, the need to learn a set of accurate rules (not a complete classification), and a very large amount of missing values. We modified the algorithm C4.5 to deal with these problems., Results: Rules were learnt which are accurate and biologically meaningful. The rules predict function of 83 ORFs of unknown function at an estimated accuracy of > or = 80%.
- Published
- 2002
- Full Text
- View/download PDF
5. The robot scientist Adam
- Author
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King, Ross D., Rowland, Jem, Liakata, Maria, Aubrey, Wayne, Markham, Magdalena, Soldatova, Larisa N., Whelan, Ken E., Clare, Amanda, Young, Mike, Sparkes, Andrew, Oliver, Stephen G., and Pir, Pinar
- Subjects
Artificial intelligence ,Technology application ,Computer science -- Evaluation ,Laboratory equipment -- Technology application ,Artificial intelligence -- Evaluation ,Laboratories -- Equipment and supplies ,Laboratories -- Technology application - Published
- 2009
6. Measuring scientific impact beyond academia: An assessment of existing impact metrics and proposed improvements.
- Author
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Ravenscroft, James, Liakata, Maria, Clare, Amanda, and Duma, Daniel
- Subjects
ALTMETRICS ,MEDICAL informatics ,SOCIAL media ,ACQUISITION of data ,SOCIAL sciences - Abstract
How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
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