Machine learning pedagogy to support the research community-Reference-Cited by-同舟云学术

Machine learning pedagogy to support the research community

Published:2021-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 ACM SIGPLAN International Symposium on SPLASH-E
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Author:

Dick Kevin¹^ORCID,Kyrollos Daniel G.¹^ORCID,Green James R.¹^ORCID

Affiliation:

1. Carleton University, Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3484272.3484964

Reference26 articles.

1. Ahmad Alimadadi Sachin Aryal Ishan Manandhar Patricia B Munroe Bina Joe and Xi Cheng. 2020. Artificial intelligence and machine learning to fight COVID-19. https://doi.org/10.1152/physiolgenomics.00029.2020 Ahmad Alimadadi Sachin Aryal Ishan Manandhar Patricia B Munroe Bina Joe and Xi Cheng. 2020. Artificial intelligence and machine learning to fight COVID-19. https://doi.org/10.1152/physiolgenomics.00029.2020

2. Emanuele Arielli. 2018. Taste and the algorithm. Studi di estetica https://doi.org/10.7413/18258646062 10.7413/18258646062 Emanuele Arielli. 2018. Taste and the algorithm. Studi di estetica https://doi.org/10.7413/18258646062 10.7413/18258646062

3. The value of feedback in forecasting competitions

4. The Client and the Cloud: Democratizing Research Computing

5. Bo Ram Beck Bonggun Shin Yoonjung Choi Sungsoo Park and Keunsoo Kang. 2020. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal 18 (2020) 784–790. https://doi.org/10.1016/j.csbj.2020.03.025 10.1016/j.csbj.2020.03.025 Bo Ram Beck Bonggun Shin Yoonjung Choi Sungsoo Park and Keunsoo Kang. 2020. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal 18 (2020) 784–790. https://doi.org/10.1016/j.csbj.2020.03.025 10.1016/j.csbj.2020.03.025

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