Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Kareem Carr"'
Autor:
Jack Wilkinson, PhD, Kellyn F Arnold, PhD, Eleanor J Murray, ScD, Maarten van Smeden, PhD, Kareem Carr, MSc, Rachel Sippy, PhD, Marc de Kamps, PhD, Andrew Beam, PhD, Stefan Konigorski, PhD, Christoph Lippert, ProfPhD, Mark S Gilthorpe, ProfPhD, Peter W G Tennant, PhD
Publikováno v:
The Lancet: Digital Health, Vol 2, Iss 12, Pp e677-e680 (2020)
Summary: Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would re
Externí odkaz:
https://doaj.org/article/ba0bc357105946ffbe5e017ed738eb9e
Publikováno v:
PLoS Computational Biology, Vol 16, Iss 11, p e1008429 (2020)
Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are
Externí odkaz:
https://doaj.org/article/b65fed8b637b4a3ab9e08cc4e3e92a9b
Publikováno v:
PLoS ONE, Vol 5, Iss 3, p e9550 (2010)
We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the se
Externí odkaz:
https://doaj.org/article/2e668454041c4836a83409d0184a7638
Autor:
Lucy D'Agostino McGowan, Hailey R. Banack, Kareem Carr, Bertha Hidalgo, Matthew P. Fox, Eleanor J Murray
Publikováno v:
American journal of epidemiology. 190(8)
The digital world in which we live is changing rapidly. The evolving media environment is having a direct impact on traditional forms of communication and knowledge translation in public health and epidemiology. Openly accessible digital media can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48f04d09cb6669603edef36bee15f6c8
https://pubmed.ncbi.nlm.nih.gov/34089048
https://pubmed.ncbi.nlm.nih.gov/34089048
Autor:
Andrew L. Beam, Kellyn F Arnold, Kareem Carr, Peter W.G. Tennant, Maarten van Smeden, Christoph Lippert, Jack Wilkinson, Marc de Kamps, Rachel Sippy, Eleanor J Murray, Mark S. Gilthorpe, Stefan Konigorski
Publikováno v:
The Lancet: Digital Health, Vol 2, Iss 12, Pp e677-e680 (2020)
Wilkinson, J, Arnold, K, Murray, E, van Smeden, M, Carr, K, Sippy, R, de Kamps, M, Beam, A, Konigorski, S, Lippert, C, Gilthorpe, M & Tennant, P 2020, ' Time to reality check the promises of machine learning-powered precision medicine ', The Lancet Digital Health, vol. 2, no. 12 . https://doi.org/10.1016/S2589-7500(20)30200-4
Wilkinson, J, Arnold, K, Murray, E, van Smeden, M, Carr, K, Sippy, R, de Kamps, M, Beam, A, Konigorski, S, Lippert, C, Gilthorpe, M & Tennant, P 2020, ' Time to reality check the promises of machine learning-powered precision medicine ', The Lancet Digital Health, vol. 2, no. 12 . https://doi.org/10.1016/S2589-7500(20)30200-4
Summary: Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::306bdda7c112c15d13d5154be90e7a9f
https://eprints.whiterose.ac.uk/167291/1/1-s2.0-S2589750020302004-main.pdf
https://eprints.whiterose.ac.uk/167291/1/1-s2.0-S2589750020302004-main.pdf
Publikováno v:
Ecological Informatics. 3:387-396
We introduce neural networks (NNs) as a method for detecting patterns and visually comparing multivariate inter-specific morphological data. Neural networks have relatively relaxed statistical assumptions, do not require a phylogeny, and can collapse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68d8bb34324026844dac38206ade0ca6
https://doi.org/10.1016/j.ecoinf.2008.08.001
https://doi.org/10.1016/j.ecoinf.2008.08.001
Publikováno v:
PLoS ONE
PLoS ONE, Vol 5, Iss 3, p e9550 (2010)
PLoS ONE, Vol 5, Iss 3, p e9550 (2010)
We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b7f84b7fee329f31dee792580a5a7de
https://pubmed.ncbi.nlm.nih.gov/20221427
https://pubmed.ncbi.nlm.nih.gov/20221427