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Deep learning: new computational modelling techniques for genomics
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Deep learning: new computational modelling techniques for genomics

Eraslan, Gökcen ; Avsec, Žiga ; Gagneur, Julien ; Theis, Fabian J

Nature reviews. Genetics, 2019-07, Vol.20 (7), p.389-403 [Periódico revisado por pares]

England: Nature Publishing Group

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Prefrontal cortex as a meta-reinforcement learning system
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Prefrontal cortex as a meta-reinforcement learning system

Wang, Jane X ; Kurth-Nelson, Zeb ; Kumaran, Dharshan ; Tirumala, Dhruva ; Soyer, Hubert ; Leibo, Joel Z ; Hassabis, Demis ; Botvinick, Matthew

Nature neuroscience, 2018-06, Vol.21 (6), p.860-868 [Periódico revisado por pares]

United States: Nature Publishing Group

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Do no harm: a roadmap for responsible machine learning for health care
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Do no harm: a roadmap for responsible machine learning for health care

Wiens, Jenna ; Saria, Suchi ; Sendak, Mark ; Ghassemi, Marzyeh ; Liu, Vincent X ; Doshi-Velez, Finale ; Jung, Kenneth ; Heller, Katherine ; Kale, David ; Saeed, Mohammed ; Ossorio, Pilar N ; Thadaney-Israni, Sonoo ; Goldenberg, Anna

Nature medicine, 2019-09, Vol.25 (9), p.1337-1340 [Periódico revisado por pares]

United States: Nature Publishing Group

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Comparing different supervised machine learning algorithms for disease prediction
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Comparing different supervised machine learning algorithms for disease prediction

Uddin, Shahadat ; Khan, Arif ; Hossain, Md Ekramul ; Moni, Mohammad Ali

BMC medical informatics and decision making, 2019-12, Vol.19 (1), p.281-281, Article 281 [Periódico revisado por pares]

England: BioMed Central Ltd

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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Chicco, Davide ; Jurman, Giuseppe

BMC genomics, 2020-01, Vol.21 (1), p.6-6, Article 6 [Periódico revisado por pares]

England: BioMed Central Ltd

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Use of machine learning to analyse routinely collected intensive care unit data: a systematic review
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Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

Shillan, Duncan ; Sterne, Jonathan A C ; Champneys, Alan ; Gibbison, Ben

Critical care (London, England), 2019-08, Vol.23 (1), p.284-284, Article 284 [Periódico revisado por pares]

England: BioMed Central Ltd

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A primer on deep learning in genomics
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A primer on deep learning in genomics

Zou, James ; Huss, Mikael ; Abid, Abubakar ; Mohammadi, Pejman ; Torkamani, Ali ; Telenti, Amalio

Nature genetics, 2019-01, Vol.51 (1), p.12-18 [Periódico revisado por pares]

United States: Nature Publishing Group

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8
Machine learning in cardiovascular magnetic resonance: basic concepts and applications
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Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Leiner, Tim ; Rueckert, Daniel ; Suinesiaputra, Avan ; Baeßler, Bettina ; Nezafat, Reza ; Išgum, Ivana ; Young, Alistair A

Journal of cardiovascular magnetic resonance, 2019-10, Vol.21 (1), p.61-61, Article 61 [Periódico revisado por pares]

England: BioMed Central Ltd

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Calibration: the Achilles heel of predictive analytics
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Calibration: the Achilles heel of predictive analytics

Van Calster, Ben ; McLernon, David J ; van Smeden, Maarten ; Wynants, Laure ; Steyerberg, Ewout W

BMC medicine, 2019-12, Vol.17 (1), p.230-230, Article 230 [Periódico revisado por pares]

England: BioMed Central Ltd

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Investigating systematic bias in brain age estimation with application to post‐traumatic stress disorders
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Investigating systematic bias in brain age estimation with application to post‐traumatic stress disorders

Liang, Hualou ; Zhang, Fengqing ; Niu, Xin

Human brain mapping, 2019-08, Vol.40 (11), p.3143-3152 [Periódico revisado por pares]

Hoboken, USA: John Wiley & Sons, Inc

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