A framework for modelling desert locust population dynamics and large-scale dispersal.

Autor: Retkute R; Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, United Kingdom., Thurston W; Met Office, Fitzroy Road, Exeter, United Kingdom., Cressman K; Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, Rome, Italy., Gilligan CA; Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, United Kingdom.

Publikováno v: PLoS computational biology [PLoS Comput Biol] 2024 Dec 19; Vol. 20 (12), pp. e1012562. Date of Electronic Publication: 2024 Dec 19 (Print Publication: 2024).

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

Autor: Bergquist T; Sage Bionetworks, Seattle, WA, United States.; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States., Schaffter T; Sage Bionetworks, Seattle, WA, United States., Yan Y; Sage Bionetworks, Seattle, WA, United States.; Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, United States., Yu T; Sage Bionetworks, Seattle, WA, United States., Prosser J; Institute of Translational Health Sciences, University of Washington, Seattle, WA, United States., Gao J; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States., Chen G; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States., Charzewski Ł; Proacta, Warsaw, Poland.; Division of Biophysics, University of Warsaw, Warsaw, Poland., Nawalany Z; Proacta, Warsaw, Poland., Brugere I; Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States., Retkute R; Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom., Prusokas A; Plant and Molecular Sciences, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom., Prusokas A; Department of Life Sciences, Imperial College London, London, United Kingdom., Choi Y; Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, Republic of Korea., Lee S; Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, Republic of Korea., Choe J; Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, Republic of Korea., Lee I; Department of Interdisciplinary Program in Bioinformatics, College of Informatics, Korea University, Seoul, Republic of Korea., Kim S; Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, Republic of Korea., Kang J; Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, Republic of Korea.; Department of Interdisciplinary Program in Bioinformatics, College of Informatics, Korea University, Seoul, Republic of Korea., Mooney SD; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States., Guinney J; Sage Bionetworks, Seattle, WA, United States.; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.

Publikováno v: Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2023 Dec 22; Vol. 31 (1), pp. 35-44.

Machine learning based lineage tree reconstruction improved with knowledge of higher level relationships between cells and genomic barcodes.

Autor: Prusokiene A; School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK., Prusokas A; Independent researcher, London, SW7 2BX, UK., Retkute R; Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK.

Publikováno v: NAR genomics and bioinformatics [NAR Genom Bioinform] 2023 Aug 21; Vol. 5 (3), pp. lqad077. Date of Electronic Publication: 2023 Aug 21 (Print Publication: 2023).

INTEGRATING GEOSTATISTICAL MAPS AND INFECTIOUS DISEASE TRANSMISSION MODELS USING ADAPTIVE MULTIPLE IMPORTANCE SAMPLING

Autor: Retkute, R, Touloupou, P, Basáñez, M-G, Hollingsworth, TD, Spencer, SEF

The Adaptive Multiple Importance Sampling algorithm (AMIS) is an iterative technique which recycles samples from all previous iterations in order to improve the efficiency of the proposal distribution. We have formulated a new statistical framework,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1285dc2f5b8d6ccbe5c6d3db489f1347
https://www.repository.cam.ac.uk/handle/1810/332683

Photoacclimation and entrainment of photosynthesis by fluctuating light varies according to genotype in Arabidopsis thaliana .

Autor: Burgess AJ; School of Biosciences, University of Nottingham, Loughborough, United Kingdom., Retkute R; Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom., Murchie EH; School of Biosciences, University of Nottingham, Loughborough, United Kingdom.

Publikováno v: Frontiers in plant science [Front Plant Sci] 2023 Mar 09; Vol. 14, pp. 1116367. Date of Electronic Publication: 2023 Mar 09 (Print Publication: 2023).