Editorial: Artificial intelligence for extracting phenotypic features and disease subtyping applied to single-cell sequencing data

Saurav Mallik; Anirban Mukhopadhyay; Aimin Li; Gabriel J Odom; Namrata Tomar

doi:10.3389/fgene.2022.1083719

Editorial: Artificial intelligence for extracting phenotypic features and disease subtyping applied to single-cell sequencing data

Front Genet. 2023 Jan 4:13:1083719. doi: 10.3389/fgene.2022.1083719. eCollection 2022.

Authors

Saurav Mallik¹, Anirban Mukhopadhyay², Aimin Li³, Gabriel J Odom⁴, Namrata Tomar⁵

Affiliations

¹ Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, United States.
² Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India.
³ School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.
⁴ Department of Biostatistics, Florida International University's Stempel College of Public Health, Miami, FL, United States.
⁵ Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, United States.

No abstract available

Keywords: artificial intelligence; biomarker discovery; classifier; clustering; dimensionality reduction; feature selection; single cell sequencing data.

Publication types

Editorial