Machine learning-based linking of bacterial genomes to optimal growth pH. The 234.2 MB dataset, authored by Huilong Chen, was last updated on April 16, 2026, and is available under an MIT license. It includes files in GZ, PY, TXT, CSV, and XLSX formats.
Use Cases
- Train a classifier to predict optimal pH for bacterial growth based on genomic features.
- Build a regression model to estimate precise pH optima from genome sequences.
- Perform feature importance analysis to identify genomic markers associated with acid or alkali tolerance.
- Benchmark new machine learning algorithms for linking genotype to environmental phenotype.
Strengths
- Dataset size is 234.2 MB, indicating a substantial collection of genomic or feature data.
- Released under a permissive MIT license, allowing for broad reuse and modification.
- Last updated date of 2026-04-16 suggests recent maintenance or versioning.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for specific modeling tasks.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- figshare, authored by Huilong Chen.
- Collection Method
- Likely involves computational analysis and machine learning to link genomic data to phenotypic pH measurements.
- Time Range
- null
- Freshness
- Last updated 2026-04-16 02:56:13.
- Geography
- null