Chlamy_ChloroPred: a deep learning-based, highly accurate binary classifier for chloroplast protein prediction in the model microalga, Chlamydomonas reinhardtii, with potential cross-proteome versatility
We developed a deep learning-based framework, Chlamy_ChloroPred, that integrates carefully designed neural layers with low computational complexity, achieving high predictive accuracy and interpretability. We believe that Chlamy_ChloroPred represents a compelling alternative to existing predictors, especially when accurate inference of chloroplast proteins is required.
Authors
- Hong Il Choi
- Yong Jae Lee (ORCID: https://orcid.org/0000-0001-9917-8199)
- Jin-Ho Yun
- Dae-Hyun Cho
- Bum-Soo Shin
- Junyoung Chun
- Il Hyung Lee
- Dong Won Lee
- Hee-Sik Kim
- Sung Ho Lee
Institutions
- RIKEN BioResource Research Center (JP)
- Instituto de la Visión (AR)
- Korea Research Institute of Bioscience and Biotechnology (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2026-02-23
- DOI
- https://doi.org/10.3389/fmicb.2026.1744805
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Korea Research Institute of Bioscience and Biotechnology
- National Research Foundation of Korea
- Centre National de la Recherche Scientifique
- Ministry of Science and ICT, South Korea