The Ontology for Biomedical Investigations.

Autor: Anita Bandrowski, Ryan Brinkman, Mathias Brochhausen, Matthew H Brush, Bill Bug, Marcus C Chibucos, Kevin Clancy, Mélanie Courtot, Dirk Derom, Michel Dumontier, Liju Fan, Jennifer Fostel, Gilberto Fragoso, Frank Gibson, Alejandra Gonzalez-Beltran, Melissa A Haendel, Yongqun He, Mervi Heiskanen, Tina Hernandez-Boussard, Mark Jensen, Yu Lin, Allyson L Lister, Phillip Lord, James Malone, Elisabetta Manduchi, Monnie McGee, Norman Morrison, James A Overton, Helen Parkinson, Bjoern Peters, Philippe Rocca-Serra, Alan Ruttenberg, Susanna-Assunta Sansone, Richard H Scheuermann, Daniel Schober, Barry Smith, Larisa N Soldatova, Christian J Stoeckert, Chris F Taylor, Carlo Torniai, Jessica A Turner, Randi Vita, Patricia L Whetzel, Jie Zheng

Publikováno v: PLoS ONE, Vol 11, Iss 4, p e0154556 (2016)

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide

Externí odkaz: https://doaj.org/article/f988ee11ce904507a3a11c59e8f2e477

Transformational machine learning

Autor: Andy M Davis, Ross D. King, Ivan Olier, Joaquin Vanschoren, Larisa N. Soldatova, Oghenejokpeme I. Orhobor, Tirtharaj Dash

Publikováno v: Proceedings of the National Academy of Sciences of the United States of America (PNAS), 118(49):e2108013118. National Academy of Sciences
Proceedings of the National Academy of Sciences of the United States of America

Significance Machine learning (ML) is the branch of artificial intelligence (AI) that develops computational systems that learn from experience. In supervised ML, the ML system generalizes from labelled examples to learn a model that can predict the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7be63c17ce2602fcaf1e5a92431c8774
https://research.tue.nl/en/publications/ade3d4b8-5ea0-4525-847b-40095127bae0

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

Autor: Aram Galstyan, Halima Alachram, Fenia Christopoulou, Edgar Wingender, Tim Beißbarth, Ross D. King, James A. Evans, Sophia Ananiadou, Emily Sheng, Bohdan B. Khomtchouk, Daniel Marcu, Maolin Li, Xin Gao, Brendan Chambers, Robert Stevens, Yu Li, Sahil Garg, Ulf Hermjakob, Larisa N. Soldatova, Kanix Wang, Andrey Rzhetsky, José Luis Ambite

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in machine-reading have followed in the wake of critical corpus develop

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::62037440ebd188b2721ada5cbf754832
https://doi.org/10.1101/2020.11.05.368969

Automating sciences: Philosophical and social dimensions

Autor: Larisa N. Soldatova, Chris Mellingwood, Vlad Schuler Costa, Ross D. King

Publikováno v: King, R D, Schuler Costa, V, Mellingwood, C & Soldatova, L N 2018, ' Automating sciences : Philosophical and social dimensions ', IEEE Technology and Society Magazine, vol. 37, no. 1, pp. 40-46 . https://doi.org/10.1109/MTS.2018.2795097

Clark Glymour argued in 2004 that "despite a lack of public fanfare, there is mounting evidence that we are in the midst of ... a revolution - premised on the automation of scientific discovery" [1]. This paper highlights some of the philosophical an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59d2a142f0515bd45b1d3a2eff43e98b

Federated Ensemble Regression Using Classification

Autor: Oghenejokpeme I. Orhobor, Ross D. King, Larisa N. Soldatova

Publikováno v: Discovery Science ISBN: 9783030615260
DS

Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2a22f52c9b42969deccb523426e25fa7
https://doi.org/10.1007/978-3-030-61527-7_22