mlsanityData to 7 Oct 2026

Generating and designing DNA with deep generative models

arXiv 1712.06148 ↗First posted 17 Dec 2017cs.LG q-bio.GN stat.MLcited by 30

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Abstract (arXiv)

We propose generative neural network methods to generate DNA sequences and tune them to have desired properties. We present three approaches: creating synthetic DNA sequences using a generative adversarial network; a DNA-based variant of the activation maximization ("deep dream") design method; and a joint procedure which combines these two approaches together. We show that these tools capture important structures of the data and, when applied to designing probes for protein binding microarrays, allow us to generate new sequences whose properties are estimated to be superior to those found in the training data. We believe that these results open the door for applying deep generative models to advance genomics research.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Dec 2017.

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Citing papers per year: 2021: 8; 2022: 3; 2023: 7; 2024: 6; 2025: 3; 2026: 3.

30 papers.

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  1. 2021WaveFake: A Data Set to Facilitate Audio Deepfake Detectioncited by 78
  2. 2023Dirichlet Diffusion Score Model for Biological Sequence Generationcited by 73
  3. 2022Conformal Prediction Under Feedback Covariate Shift for Biomolecular Designcited by 53
  4. 2021Offline Model-Based Optimization via Normalized Maximum Likelihood Estimationcited by 38
  5. 2022Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMCcited by 17
  6. 2024DiscDiff: Latent Diffusion Model for DNA Sequence Generationcited by 12
  7. 2021Partition-Guided GANscited by 9
  8. 2021On Utility and Privacy in Synthetic Genomic Datacited by 8
  9. 2023Latent Diffusion Model for DNA Sequence Generationcited by 8
  10. 2021Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogatecited by 6
  11. 2022Fourier Representations for Black-Box Optimization over Categorical Variablescited by 4
  12. 2023Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasetscited by 4
18 more
  1. 2021Improving RNA Secondary Structure Design using Deep Reinforcement Learningcited by 2
  2. 2023Is novelty predictable?cited by 2
  3. 2023Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formulacited by 1
  4. 2024From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimizationcited by 1
  5. 2024Reinforcement Learning for Sequence Design Leveraging Protein Language Modelscited by 1
  6. 2025Language Models for Controllable DNA Sequence Designcited by 1
  7. 2025Designing ReLU Generative Networks to Enumerate Trees with a Given Tree Edit Distancecited by 1
  8. 2021Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problemcited by 0
  9. 2021Differential-Critic GAN: Generating What You Want by a Cue of Preferencescited by 0
  10. 2023T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapycited by 0
  11. 2023Description Generation using Variational Auto-Encoders for precursor microRNAcited by 0
  12. 2024Semantically Rich Local Dataset Generation for Explainable AI in Genomicscited by 0
  13. 2024Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletabilitycited by 0
  14. 2024Absorb & Escape: Overcoming Single Model Limitations in Generating Genomic Sequencescited by 0
  15. 2025Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only datacited by 0
  16. 2026Enhanced Data-Driven Product Development via Gradient Based Optimization and Conformalized Monte Carlo Dropout Uncertainty Estimationcited by 0
  17. 2026ReLU Networks for Exact Generation of Similar Graphscited by 0
  18. 2026Synthetic data in cryptocurrencies using generative modelscited by 0