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- DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome - GitHub - jerryji1993/DNABERT: DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- A Stanford researcher questions the need for generalizable models and proposes instead sharing recipes for creating useful local models.
- Get the best language feedback and big data widgets. Writefull's AI language models have been trained on millions of journal articles.
publications (hide)5688
- Statistics and computing 14 (3): 199--222 (2004)
- Technometrics 12 (1): 55--67 (1970)
- Bioinformatics 37 (15): 2112-2120 (February 2021)
- Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021) , page 170--181. (2021)
- Multivariate behavioral research 46 (3): 399--424 (2011)
- arXiv preprint arXiv:2012.09699 (2020)
- Nature Reviews Molecular Cell Biology 23 (1): 40--55 (2022)
- Proceedings of the IEEE 109 (3): 247--278 (2021)
- (2006)
- European Semantic Web Conference , page 3--20. Springer, (2022)
- Ai Magazine 43 (1): 46--58 (2022)
- Clinical Pharmacology & Therapeutics 92 (4): 414--417 (2012)
- 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , page 3386--3391. IEEE, (2021)
- (2022)cite arxiv:2203.08913Comment: Published as a conference paper at ICLR 2022 (spotlight).
- Frontiers in Bioinformatics (May 2022)
- Virtual reality 23 (1): 1--15 (2019)
- Proc. 18th International Conf. on Machine Learning , page 282--289. Morgan Kaufmann, San Francisco, CA, (2001)
- 2021 International Conference on Data Mining Workshops (ICDMW) , page 10-17. (December 2021)
- iScience 23 (11): 101656 (2020)
- Knowledge-Based Systems (2021)
