Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites-Reference-Cited by-同舟云学术

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Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites

Published:2023-10-13 Issue:42 Volume:8 Page:39420-39426
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Le Nguyen Quoc Khanh¹²³⁴^ORCID,Xu Ling⁵

Affiliation:

1. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan

2. Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan

3. AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan

4. Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan

5. NUS-ISS, National University of Singapore, Singapore, 119615, Singapore

Funder

National Science and Technology Council

Ministry of Education, Taiwan

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c05074

Reference26 articles.

1. Computational identification of N6-methyladenosine sites in multiple tissues of mammals

2. Synthesis and turnover of nuclear and cytoplasmic polyadenylic acid in mouse L cells

3. N6-Methyladenosine in RNA and DNA: An Epitranscriptomic and Epigenetic Player Implicated in Determination of Stem Cell Fate

4. Role of N6-methyladenosine modification in cancer

5. Yeast targets for mRNA methylation

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