Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914567733379072 |
|---|---|
| author | Wu, Weimin Song, Xuefeng Wen, Yibo Lin, Qinjie Zhou, Zhihan Hu, Jerry Yao-Chieh Wang, Zhong Liu, Han |
| author_facet | Wu, Weimin Song, Xuefeng Wen, Yibo Lin, Qinjie Zhou, Zhihan Hu, Jerry Yao-Chieh Wang, Zhong Liu, Han |
| contents | We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core contribution is to simplify and unify the workflow for genomic model development: data collection, model tuning, inference, benchmarking, and interpretability. For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them. For model tuning, Genome-Factory supports both full and parameter-efficient fine-tuning across diverse genomic models. For inference, Genome-Factory enables both embedding extraction and DNA sequence generation. For benchmarking, we include two existing benchmarks and provide a flexible interface to incorporate additional benchmarks. For interpretability, Genome-Factory introduces an open-source biological interpreter based on a sparse auto-encoder. We validate the utility of Genome-Factory across three dimensions: (i) Compatibility with diverse models and fine-tuning methods; (ii) Benchmarking downstream performance using two open-source benchmarks; (iii) Biological interpretation of learned representations with DNABERT-2. These results highlight its practical value for real-world genomic analysis. GitHub: https://github.com/WeiminWu2000/Genome_Factory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12266 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models Wu, Weimin Song, Xuefeng Wen, Yibo Lin, Qinjie Zhou, Zhihan Hu, Jerry Yao-Chieh Wang, Zhong Liu, Han Genomics Machine Learning We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core contribution is to simplify and unify the workflow for genomic model development: data collection, model tuning, inference, benchmarking, and interpretability. For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them. For model tuning, Genome-Factory supports both full and parameter-efficient fine-tuning across diverse genomic models. For inference, Genome-Factory enables both embedding extraction and DNA sequence generation. For benchmarking, we include two existing benchmarks and provide a flexible interface to incorporate additional benchmarks. For interpretability, Genome-Factory introduces an open-source biological interpreter based on a sparse auto-encoder. We validate the utility of Genome-Factory across three dimensions: (i) Compatibility with diverse models and fine-tuning methods; (ii) Benchmarking downstream performance using two open-source benchmarks; (iii) Biological interpretation of learned representations with DNABERT-2. These results highlight its practical value for real-world genomic analysis. GitHub: https://github.com/WeiminWu2000/Genome_Factory. |
| title | Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models |
| topic | Genomics Machine Learning |
| url | https://arxiv.org/abs/2509.12266 |