Development of multiple microbiome biomarkers using penalized regression methods
This study presents a unified approach for identifying multiple microbiome biomarkers using penalized regression for clinical outcome prediction. The proposed methods are applied to both continuous and binary outcomes. The method enhances the detection of meaningful biomarkers with potential for personalized treatment and disease management.
Authors
- Davit Sargsyan (ORCID: https://orcid.org/0000-0003-1594-6715)
- Rudradev Sengupta (ORCID: https://orcid.org/0000-0002-2656-1979)
- Dhammika Amaratunga (ORCID: https://orcid.org/0000-0001-8293-5042)
- Michael N. Katehakis (ORCID: https://orcid.org/0000-0002-1511-7098)
- Markus Kleinewietfeld (ORCID: https://orcid.org/0000-0002-2832-3149)
- Olajumoke Evangelina Owokotomo
- Ziv Shkedy (ORCID: https://orcid.org/0000-0002-6306-0823)
- Javier Cabrera (ORCID: https://orcid.org/0009-0000-9771-0786)
- Thi Huyen Nguyen (ORCID: https://orcid.org/0009-0007-5128-8605)
- Ibrahim Hamad (ORCID: https://orcid.org/0000-0002-0234-5024)
Institutions
- Rutgers, The State University of New Jersey (US)
- Johnson & Johnson (United States) (US)
- Clinique Saint Pierre (BE)
- Rutgers Sexual and Reproductive Health and Rights (NL)
- EP Analytics (United States) (US)
- GlaxoSmithKline (Switzerland) (CH)
- Hasselt University (BE)
Publication Details
- Journal
- Journal of Applied Microbiology
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1093/jambio/lxaf242
- Citations
- 1
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 1.05