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Träfflista för sökning "WFRF:(Patel Vinay) srt2:(2020-2024)"

Search: WFRF:(Patel Vinay) > (2020-2024)

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1.
  • Abbafati, Cristiana, et al. (author)
  • 2020
  • Journal article (peer-reviewed)
  •  
2.
  • Menkveld, Albert J., et al. (author)
  • Nonstandard Errors
  • 2024
  • In: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
  • Journal article (peer-reviewed)abstract
    • In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
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3.
  • Reder, Gabriel, 1992, et al. (author)
  • Genesis-DB: a database for autonomous laboratory systems
  • 2023
  • In: Bioinformatics Advances. - 2635-0041. ; 3:1
  • Journal article (peer-reviewed)abstract
    • Artificial intelligence (AI)-driven laboratory automation - combining robotic labware and autonomous software agents - is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond.
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  • Result 1-3 of 3
Type of publication
journal article (3)
Type of content
peer-reviewed (3)
Author/Editor
Johansson, Lars (1)
Wolf, Michael (1)
Sulo, Gerhard (1)
King, Ross, 1962 (1)
Hassankhani, Hadi (1)
Liu, Yang (1)
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Ali, Muhammad (1)
Mitchell, Philip B (1)
McKee, Martin (1)
Madotto, Fabiana (1)
Abolhassani, Hassan (1)
Rezaei, Nima (1)
Castro, Franz (1)
Koul, Parvaiz A. (1)
Weiss, Daniel J. (1)
Ackerman, Ilana N. (1)
Szaszi, Barnabas (1)
Dreber Almenberg, An ... (1)
Holzmeister, Felix (1)
Huber, Juergen (1)
Johannesson, Magnus (1)
Kirchler, Michael (1)
Brenner, Hermann (1)
Ferrara, Giannina (1)
Salama, Joseph S. (1)
Mullany, Erin C. (1)
Abbafati, Cristiana (1)
Bensenor, Isabela M. (1)
Bernabe, Eduardo (1)
Carrero, Juan J. (1)
Cercy, Kelly M. (1)
Zaki, Maysaa El Saye ... (1)
Esteghamati, Alireza (1)
Esteghamati, Sadaf (1)
Fanzo, Jessica (1)
Farzadfar, Farshad (1)
Foigt, Nataliya A. (1)
Grosso, Giuseppe (1)
Islami, Farhad (1)
James, Spencer L. (1)
Khader, Yousef Saleh (1)
Kimokoti, Ruth W. (1)
Kumar, G. Anil (1)
Lallukka, Tea (1)
Lotufo, Paulo A. (1)
Mendoza, Walter (1)
Nagel, Gabriele (1)
Nguyen, Cuong Tat (1)
Nixon, Molly R. (1)
Ong, Kanyin L. (1)
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University
University of Gothenburg (1)
Uppsala University (1)
Stockholm University (1)
Stockholm School of Economics (1)
Chalmers University of Technology (1)
Karolinska Institutet (1)
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Högskolan Dalarna (1)
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Language
English (3)
Research subject (UKÄ/SCB)
Natural sciences (2)
Engineering and Technology (1)
Medical and Health Sciences (1)
Social Sciences (1)

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