KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model.
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| Title: | KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model. |
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| Authors: | Gordon M; Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Drive, Raleigh, NC, 27607, USA., Akyol TY; Department of Molecular Biology and Genetics, Aarhus University, Universitetsbyen 81, Aarhus C, 8000, Denmark., Amos B; Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Drive, Raleigh, NC, 27607, USA., Andersen SU; Department of Molecular Biology and Genetics, Aarhus University, Universitetsbyen 81, Aarhus C, 8000, Denmark., Williams C; Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Drive, Raleigh, NC, 27607, USA. cmwilli5@ncsu.edu. |
| Source: | BioData mining [BioData Min] 2026 Feb 19; Vol. 19 (1), pp. 18. Date of Electronic Publication: 2026 Feb 19. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101319161 Publication Model: Electronic Cited Medium: Print ISSN: 1756-0381 (Print) Linking ISSN: 17560381 NLM ISO Abbreviation: BioData Min Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1756-0381 |
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| DOI: | 10.1186/s13040-026-00527-3 |
