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This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refine these sequences.
The eukaryotic promoter database epd
Périer, R. C., Junier, T., and Bucher, P · 1998
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J. and Troyanskaya, O. G · 2015
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Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Generating and designing dna with deep generative models
Killoran, N., Lee, L. J., Delong, A., Duvenaud, D., and Frey, B. J · 2017
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., and Snoek, J · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., and Melville, J · 2018
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Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., and Theis, F. J · 2019
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Feedback gan for dna optimizes protein functions
Gupta, A. and Zou, J · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Cross-species regulatory sequence activity prediction
Kelley, D. R · 2020
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Epd in 2020: enhanced data visualization and extension to ncrna promoters
Meylan, P., Dreos, R., Ambrosini, G., Groux, R., and Bucher, P · 2020
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Synthetic promoter design in escherichia coli based on a deep generative network
Wang, Y., Wang, H., Wei, L., Li, S., Liu, L., and Wang, X · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and Van Den Berg, R · 2021
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Effective gene expression prediction from sequence by integrating long-range interactions
Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., and Kelley, D. R · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Learning to break the loop: Analyzing and mitigating repetitions for neural text generation
Xu, J., Liu, X., Yan, J., Cai, D., Li, H., and Li, J · 2022
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Controlling gene expression with deep generative design of regulatory dna
Zrimec, J., Fu, X., Muhammad, A. S., Skrekas, C., Jauniskis, V., Speicher, N. K., Börlin, C. S., Verendel, V., Chehreghani, M. H., Dubhashi, D., et al · 2022
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Protein generation with evolutionary diffusion: sequence is all you need
Alamdari, S., Thakkar, N., van den Berg, R., Lu, A. X., Fusi, N., Amini, A. P., and Yang, K. K · 2023
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Dirichlet diffusion score model for biological sequence generation
Avdeyev, P., Shi, C., Tan, Y., Dudnyk, K., and Zhou, J · 2023
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Dhariwal, P. and Nichol, A · 2021
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Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome
Ji, Y., Zhou, Z., Liu, H., and Davuluri, R. V · 2021
Cited alongside, same era.
Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
Cited alongside, same era.
Analog bits: Generating discrete data using diffusion models with self-conditioning
Chen, T., Zhang, R., and Hinton, G · 2022
Cited alongside, same era.
Continuous diffusion for categorical data
Dieleman, S., Sartran, L., Roshannai, A., Savinov, N., Ganin, Y., Richemond, P. H., Doucet, A., Strudel, R., Dyer, C., Durkan, C., et al · 2022
Cited alongside, same era.
Han, X., Kumar, S., and Tsvetkov, Y · 2022
Cited alongside, same era.
Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., and Fleet, D. J · 2022
Cited alongside, same era.
The nucleotide transformer: Building and evaluating robust foundation models for human genomics
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Carranza, N. L., Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., de Almeida, B. P., Sirelkhatim, H., et al · 2023
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Genomic benchmarks: a collection of datasets for genomic sequence classification
Grešová, K., Martinek, V., Čechák, D., Šimeček, P., and Alexiou, P · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 2023
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Photorealistic video generation with diffusion models
Gupta, A., Yu, L., Sohn, K., Gu, X., Hahn, M., Fei-Fei, L., Essa, I., Jiang, L., and Lezama, J · 2023
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reglm: Designing realistic regulatory dna with autoregressive language models
Lal, A., Biancalani, T., and Eraslan, G · 2023
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Genomic interpreter: A hierarchical genomic deep neural network with 1d shifted window transformer
Li, Z., Das, A., Beardall, W. A., Zhao, Y., and Stan, G.-B · 2023
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Audioldm: Text-to-audio generation with latent diffusion models
Liu, H., Chen, Z., Yuan, Y., Mei, X., Liu, X., Mandic, D., Wang, W., and Plumbley, M. D · 2023
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Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution
Nguyen, E., Poli, M., Faizi, M., Thomas, A., Birch-Sykes, C., Wornow, M., Patel, A., Rabideau, C., Massaroli, S., Bengio, Y., et al · 2023
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Cell type directed design of synthetic enhancers
Taskiran, I. I., Spanier, K. I., Dickmänken, H., Kempynck, N., Pančíková, A., Ekşi, E. C., Hulselmans, G., Ismail, J. N., Theunis, K., Vandepoel, R., et al · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2023
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Score normalization for a faster diffusion exponential integrator sampler
Xia, G., Danier, D., Das, A., Fotiadis, S., Nabiei, F., Sengupta, U., and Bernacchia, A · 2023
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2023
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Planner: Generating diversified paragraph via latent language diffusion model
Zhang, Y., Gu, J., Wu, Z., Zhai, S., Susskind, J., and Jaitly, N · 2023
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