[32] J. Meier, R. Rao, R. Verkuil, J. Liu, T. Sercu, and A. Rives. Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems, 34:29287-29303, 2021.
[33] J. Nasser, D. T. Bergman, C. P. Fulco, P. Guckelberger, B. R. Doughty, T. A. Patwardhan, T. R. Jones, T. H. Nguyen, J. C. Ulirsch, F. Lekschas, K. Mualim, H. M. Natri, E. M. Weeks, G. Munson, M. Kane, H. Y. Kang, A. Cui, J. P. Ray, T. M. Eisenhaure, R. L. Collins, K. Dey, H. Pfister, A. L. Price, C. B. Epstein, A. Kundaje, R. J. Xavier, M. J. Daly, H. Huang, H. K. Finucane, N. Hacohen, E. S. Lander, and J. M. Engreitz. Genome-wide enhancer maps link risk variants to disease genes. Nature, 593:238-243, 2021.
[34] M. Oubounyt, Z. Louadi, H. Tayara, and K. T. Chong. DeePromoter: Robust promoter predictor using deep learning. Frontiers in Genetics, 10, 2019.
[35] T. H. Pham, D. H. Tran, T. B. H. Ho, K. Satou, and G. Valiente. Qualitatively predicting acetylation and methylation areas in DNA sequences. Genome Informatics, 16(2):3-11, 2005.
[36] D. K. Pokholok, C. T. Harbison, S. Levine, F. Lewitter, D. K. Gifford, and R. A. Young. Genome-wide map of nucleosome acetylation and methylation in yeast. Cell, 122(4):517-527, 2005.
[37] M. Poli, S. Massaroli, E. Nguyen, D. Y. Fu, T. Dao, S. Baccus, Y. Bengio, S. Ermon, and C. Ré. Hyena Hierarchy: Towards larger convolutional language models. arXiv preprint arXiv:2302.10866, 2023.
[38] O. Press, N. A. Smith, and M. Lewis. Shortformer: Better language modeling using shorter inputs. arXiv preprint arXiv:2012.15832, 2020.
[39] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[40] A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821-8831. PMLR, 2021.
[41] R. Rao, J. Meier, T. Sercu, S. Ovchinnikov, and A. Rives. Transformer protein language models are unsupervised structure learners. Biorxiv, pages 2020-12, 2020.
[42] Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature, 518(7539):317-330, 2015.
[43] J. Rogers and R. A. Gibbs. Comparative primate genomics: emerging patterns of genome content and dynamics. Nature Reviews Genetics, 15(5):347-359, 2014.
[44] D. W. Romero, R.-J. Bruintjes, J. M. Tomczak, E. J. Bekkers, M. Hoogendoorn, and J. C. van Gemert. Flexconv: Continuous kernel convolutions with differentiable kernel sizes. arXiv preprint arXiv:2110.08059, 2021.
[45] D. W. Romero, A. Kuzina, E. J. Bekkers, J. M. Tomczak, and M. Hoogendoorn. Ckconv: Continuous kernel convolution for sequential data. arXiv preprint arXiv:2102.02611, 2021.
[46] N. Scalzitti, A. Kress, R. Orhand, T. Weber, L. Moulinier, A. Jeannin-Girardon, O. Collet, Pierre anf Poch, and J. D. Thompson. Spliceator: multi-species splice site prediction using convolutional neural networks. BMC Bioinformatics, 22(1):1-26, 2021.
[47] J. T. Smith, A. Warrington, and S. W. Linderman. Simplified state space layers for sequence modeling. arXiv preprint arXiv:2208.04933, 2022.
[48] Y. Tay, M. Dehghani, S. Abnar, Y. Shen, D. Bahri, P. Pham, J. Rao, L. Yang, S. Ruder, and D. Metzler. Long range arena: A benchmark for efficient transformers. arXiv preprint arXiv:2011.04006, 2020.