Relevant papers to read...

1. Ronquist S, Patterson G, Muir LA, Lindsly S, Chen H, Brown M, Wicha MS, Bloch A, Brockett R, Rajapakse I. "Algorithm for cellular reprogramming." Proceedings of the National Academy of Sciences. 2017 Nov 7;114(45):11832-7. Data-guided Control (DGC) Supporting Information

2. Chen H, Chen J, Muir LA, Ronquist S, Meixner W, Ljungman M, Ried T, Smale S, Rajapakse I. "Functional Organization of the Human 4D Nucleome. " Proceedings of the National Academy of Sciences 112.26 (2015): 8002-8007. Supporting Information 

3. Böttcher, Lucas, Nino Antulov-Fantulin, and Thomas Asikis. "AI Pontryagin or how artificial neural networks learn to control dynamical systems." Nature Communications 13.1 (2022): 333. 

4. Yang, Fan, et al. "scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data." Nature Machine Intelligence 4.10 (2022): 852-866. 

5. Geshkovski, Borjan, et al. "The emergence of clusters in self-attention dynamics." arXiv preprint arXiv:2305.05465 (2023). 

6. Chen, Ricky TQ, et al. "Neural ordinary differential equations." Advances in neural information processing systems 31 (2018). 

7. Feng, Jiarui, et al. "PathFinder: a novel graph transformer model to infer multi-cell intra-and inter-cellular signaling pathways and communications." bioRxiv (2024): 2024-01. 

8. Consens, Micaela E., et al. "To Transformers and Beyond: Large Language Models for the Genome." arXiv preprint arXiv:2311.07621 (2023). 

9. Khan, Sumeer Ahmad, et al. "Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers." Nature Machine Intelligence 5.12 (2023): 1437-1446. 

10. Geshkovski, Borjan, et al. "A mathematical perspective on Transformers." arXiv preprint arXiv:2312.10794 (2023). 

11. Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).

12. Schubert, Ingmar, et al. "A Generalist Dynamics Model for Control." arXiv preprint arXiv:2305.10912 (2023).

13. Dotson, Gabrielle A., Can Chen, Stephen Lindsly, Anthony Cicalo, Sam Dilworth, Charles Ryan, Sivakumar Jeyarajan et al. "Deciphering Multi-way Interactions in the Human Genome." Nature Communications 13, no. 1 (2022): 5498. 

14. He, Zhen, et al. "Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS." Nature Biotechnology (2024): 1-12.

15. Li, Q., Hu, Z., Wang, Y., Li, L., Fan, Y., King, I., ... & Li, Y. (2024). "Progress and Opportunities of Foundation Models in Bioinformatics." arXiv preprint arXiv:2402.04286.

16. K. Kawaharazuka, T. Matsushima, A. Gambardella, J. Guo, C. Paxton, and A. Zeng, “Real-World Robot Applications of Foundation Models: A Review.” arXiv, Feb. 08, 2024

17. Gemini Team et al., “Gemini: A Family of Highly Capable Multimodal Models.” arXiv, Dec. 18, 2023

18. C. V. Theodoris et al., “Transfer learning enables predictions in network biology,” Nature, vol. 618, no. 7965, pp. 616–624, Jun. 2023

19. W. Hou and Z. Ji, “Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis,” Nat Methods, pp. 1–4, Mar. 2024

20. H. Cui et al., “scGPT: toward building a foundation model for single-cell multi-omics using generative AI,” Nat Methods, pp. 1–11, Feb. 2024

21. V. Sharma and V. Raman, “A reliable knowledge processing framework for combustion science using foundation models,” Energy and AI, vol. 16, p. 100365, May 2024

22. X. Qiu et al., “Mapping transcriptomic vector fields of single cells,” Cell, p. S0092867421015774, Feb. 2022.

23. Schiebinger, Geoffrey, et al. "Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming." Cell 176.4 (2019): 928-943. 

24. A. Gayoso et al., “Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells,” Nat Methods, vol. 21, no. 1, pp. 50–59, Jan. 2024

25. Luck, Katja, et al. "A reference map of the human binary protein interactome." Nature (2020): 1-7. 

26. Szklarczyk, Damian, et al. "STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets." Nucleic acids research 47.D1 (2019): D607-D613.

27. R. Eguchi, M. Hamano, M. Iwata, T. Nakamura, S. Oki, and Y. Yamanishi, “TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach,” Bioinformatics, vol. 38, no. 10, pp. 2839–2846, May 2022

28. F. M. Mione et al., “A workflow management system for reproducible and interoperable high-throughput self-driving experiments,” Computers & Chemical Engineering, vol. 187, p. 108720, Aug. 2024

29. S. D. Rihm et al., “The digital lab manager: Automating research support,” SLAS Technology, vol. 29, no. 3, p. 100135, Jun. 2024: SUPPLEMENTARY INFORMATION

30. M. Hao et al., “Large Scale Foundation Model on Single-cell Transcriptomics.” bioRxiv, p. 2023.05.29.542705, Jun. 02, 2023

31. Chiliński, Mateusz, and Dariusz Plewczynski. "HiCDiffusion-diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences." bioRxiv (2024): 2024-02. 

32. C. Fang et al., “Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model.” bioRxiv, p. 2024.06.04.597354, Jun. 06, 2024

33. Boiko, Daniil A., et al. "Autonomous chemical research with large language models." Nature 624.7992 (2023): 570-578. 

34. Zhao, Hongyu, et al. "Evaluating the Utilities of Large Language Models in Single-cell Data Analysis." (2023). 

35. Paaß, Gerhard, and Sven Giesselbach. Foundation models for natural language processing: Pre-trained language models integrating media. Springer Nature, 2023. 

36. Abramson, Josh, et al. "Accurate structure prediction of biomolecular interactions with AlphaFold 3." Nature (2024): 1-3. 

37. Kanda, Genki N., et al. "Robotic search for optimal cell culture in regenerative medicine." Elife 11 (2022): e77007. 

38. Bai, Jiaru, et al. "A dynamic knowledge graph approach to distributed self-driving laboratories." Nature Communications 15.1 (2024): 462. 

39. Rihm, Simon D., et al. "Transforming research laboratories with connected digital twins." Nexus 1.1 (2024). (Technical Report)

40. Criscitiello, Christopher, et al. "Synchronization on circles and spheres with nonlinear interactions." arXiv preprint arXiv:2405.18273 (2024).

41. Silver, David, et al. "Mastering the game of go without human knowledge." nature 550.7676 (2017): 354-359. 

42. Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." nature 529.7587 (2016): 484-489. 

43. Trapnell, Cole. "Defining cell types and states with single-cell genomics." Genome research 25.10 (2015): 1491-1498. 

44. Qiu, Xiaojie, et al. "Reversed graph embedding resolves complex single-cell trajectories." Nature methods 14.10 (2017): 979-982. 

45. Rafelski, Susanne M., and Julie A. Theriot. "Establishing a conceptual framework for holistic cell states and state transitions." Cell 187.11 (2024): 2633-2651.

46. Wang, Junlin, et al. "Mixture-of-Agents Enhances Large Language Model Capabilities." arXiv preprint arXiv:2406.04692 (2024).

47. Geneva, Nicholas, and Nicholas Zabaras. "Transformers for modeling physical systems." Neural Networks 146 (2022): 272-289. 

48. Liu, Jiajia, et al. "Large language models in bioinformatics: applications and perspectives." ArXiv (2024). 

49. Davis, Jared Quincy, et al. "Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design." arXiv preprint arXiv:2407.16831 (2024). 

50. Szałata, Artur, et al. "Transformers in single-cell omics: a review and new perspectives." Nature Methods 21.8 (2024): 1430-1443.

51. Trapnell, Cole, et al. "The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells." Nature biotechnology 32.4 (2014): 381-386.

52. Lotfollahi, Mohammad. "Toward learning a foundational representation of cells and genes." Nature Methods 21.8 (2024): 1416-1417. 

53. Tan, Jimin, et al. "Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening." Nature biotechnology 41.8 (2023): 1140-1150. 

54. Sexton, Tom, and Giacomo Cavalli. "The role of chromosome domains in shaping the functional genome." Cell 160.6 (2015): 1049-1059. 

55. Simon, Elana, Kyle Swanson, and James Zou. "Language models for biological research: a primer." Nature Methods 21.8 (2024): 1422-1429. 

56. Rood, Jennifer E., Anna Hupalowska, and Aviv Regev. "Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas." Cell 187.17 (2024): 4520-4545. 

57. Roohani, Yusuf, et al. "BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments." arXiv preprint arXiv:2405.17631 (2024). 

58. Ivanov, Igor. "BioLP-bench: Measuring understanding of AI models of biological lab protocols." bioRxiv (2024): 2024-08. 

59. H. Chen et al., “Quantized multi-task learning for context-specific representations of gene network dynamics,” Aug. 19, 2024

60. Silver, David, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot et al. "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play." Science 362, no. 6419 (2018): 1140-1144.

61. Schrittwieser, Julian, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez et al. "Mastering atari, go, chess and shogi by planning with a learned model." Nature 588, no. 7839 (2020): 604-609.

62. Maheshwari, Paridhi, et al. "TimeGraphs: Graph-based Temporal Reasoning." arXiv preprint arXiv:2401.03134 (2024). 

63. Zhang, Ke, et al. "Prediction of gene co-expression from chromatin contacts with graph attention network." Bioinformatics 38.19 (2022): 4457-4465. 

64. J. M. Replogle et al., “Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq,” Cell, vol. 185, no. 14, pp. 2559-2575.e28, Jul. 2022

65. Y. Roohani, K. Huang, and J. Leskovec, “Predicting transcriptional outcomes of novel multigene perturbations with GEARS,” Nat Biotechnol, vol. 42, no. 6, pp. 927–935, Jun. 2024

66. D. Molho et al., “Deep Learning in Single-cell Analysis,” ACM Trans. Intell. Syst. Technol., vol. 15, no. 3, p. 40:1-40:62, Mar. 2024

67. S. Gao et al., “Empowering biomedical discovery with AI agents,” Cell, vol. 187, no. 22, pp. 6125–6151, Oct. 2024

68. V. Zambaldi et al., “De novo design of high-affinity protein binders with AlphaProteo,” Sep. 12, 2024, arXiv: arXiv:2409.08022

69. A. Kabir et al., “DNA breathing integration with deep learning foundational model advances genome-wide binding prediction of human transcription factors,” Nucleic Acids Research, vol. 52, no. 19, p. e91, Oct. 2024

70. de Lima Camillo, Lucas Paulo, Raghav Sehgal, Jenel Armstrong, Albert Tzongyang Higgins-Chen, Steve Horvath, and Bo Wang. "CpGPT: a Foundation Model for DNA Methylation." bioRxiv (2024): 2024-10. 

71. Schaefer, Moritz, Peter Peneder, Daniel Malzl, Mihaela Peycheva, Jake Burton, Anna Hakobyan, Varun Sharma et al. "Multimodal learning of transcriptomes and text enables interactive single-cell RNA-seq data exploration with natural-language chats." bioRxiv (2024): 2024-10. 

72. Doctor, Yesh, Milan Sanghvi, and Prashant Mali. "A Manual for Genome and Transcriptome Engineering." IEEE Reviews in Biomedical Engineering (2024). 

73. A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” Jun. 03, 2021, arXiv: arXiv:2010.11929

74. A. Singh et al., “FLAVA: A Foundational Language And Vision Alignment Model,” Mar. 29, 2022, arXiv: arXiv:2112.04482

75. Kamimoto, Kenji, Blerta Stringa, Christy M. Hoffmann, Kunal Jindal, Lilianna Solnica-Krezel, and Samantha A. Morris. "Dissecting cell identity via network inference and in silico gene perturbation." Nature 614, no. 7949 (2023): 742-751. NEWS AND VIEWS: An oracle predicts regulators of cell identity

76. Dekker, Job, Marc A. Marti-Renom, and Leonid A. Mirny. "Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data." Nature Reviews Genetics 14, no. 6 (2013): 390-403. 

77. Lajoie, Bryan R., Job Dekker, and Noam Kaplan. "The Hitchhiker’s guide to Hi-C analysis: practical guidelines." Methods 72 (2015): 65-75. 

78. Smaruj, Paulina N., Yao Xiao, and Geoffrey Fudenberg. "Recipes and ingredients for deep learning models of 3D genome folding." Current Opinion in Genetics & Development 91 (2025): 102308. 

79. Schuette, Greg, Zhuohan Lao, and Bin Zhang. "ChromoGen: Diffusion model predicts single-cell chromatin conformations." Science Advances 11, no. 5 (2025): eadr8265. 

80. Almassalha, Luay M., Marcelo Carignano, Emily Pujadas Liwag, Wing Shun Li, Ruyi Gong, Nicolas Acosta, Cody L. Dunton et al. "Chromatin conformation, gene transcription, and nucleosome remodeling as an emergent system." Science Advances 11, no. 2 (2025): eadq6652. 

81. Zhang, Zhenhao, Fan Feng, Yiyang Qiu, and Jie Liu. "A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome." Nucleic Acids Research 51, no. 12 (2023): 5931-5947. 

82. Wang, Xiao, Yuanyuan Zhang, Suhita Ray, Anupama Jha, Tangqi Fang, Shengqi Hang, Sergei Doulatov, William Stafford Noble, and Sheng Wang. "A generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species." bioRxiv (2024). 

83. Gu, Albert, and Tri Dao. "Mamba: Linear-time sequence modeling with selective state spaces." arXiv preprint arXiv:2312.00752 (2023). 

84. Tancik, Matthew, et al. "Fourier features let networks learn high frequency functions in low dimensional domains." Advances in neural information processing systems 33 (2020): 7537-7547. 

85. Kovachki, Nikola, et al. "Neural operator: Learning maps between function spaces with applications to PDEs." Journal of Machine Learning Research 24.89 (2023): 1-97. 

86. Tian, Keyu, et al. "Visual autoregressive modeling: Scalable image generation via next-scale prediction." Advances in neural information processing systems 37 (2024): 84839-84865.

87. Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 2019.

88. He, Kaiming, et al. "Masked autoencoders are scalable vision learners." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

89. R. Sheinin, R. Sharan, and A. Madi, “scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein–protein interactions,” Nat Methods, pp. 1–9, Mar. 2025, doi: 10.1038/s41592-025-02627-0.

90. M. E. Consens et al., “Transformers and genome language models,” Nat Mach Intell, Mar. 2025

91. K. Bałazy, M. Banaei, K. Aberer, and J. Tabor, “LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters,” Oct. 08, 2024, arXiv: arXiv:2405.17604.

92. C. Feng et al., “TriLoRA: Integrating SVD for Advanced Style Personalization in Text-to-Image Generation,” Jun. 13, 2024, arXiv: arXiv:2405.11236.

93. Almassalha, Luay M., Marcelo Carignano, Emily Pujadas Liwag, Wing Shun Li, Ruyi Gong, Nicolas Acosta, Cody L. Dunton et al. "Chromatin conformation, gene transcription, and nucleosome remodeling as an emergent system." Science Advances 11, no. 2 (2025): eadq6652.

94. Bommasani, Rishi, et al. "On the opportunities and risks of foundation models." arXiv preprint arXiv:2108.07258 (2021).

95. Y. Zhang, L. Boninsegna, M. Yang, T. Misteli, F. Alber, and J. Ma, “Computational methods for analysing multiscale 3D genome organization,” Nat Rev Genet, vol. 25, no. 2, pp. 123–141, Feb. 2024

96. Liang, Huan, Bonnie Berger, and Rohit Singh. "Tracing the Shared Foundations of Gene Expression and Chromatin Structure." bioRxiv (2025): 2025-03.

97. Fu, Xi, Shentong Mo, Alejandro Buendia, Anouchka P. Laurent, Anqi Shao, Maria del Mar Alvarez-Torres, Tianji Yu et al. "A foundation model of transcription across human cell types." Nature 637, no. 8047 (2025): 965-973. 

98. A. Tong, J. Huang, G. Wolf, D. van Dijk, and S. Krishnaswamy, “TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics,” July 26, 2020, arXiv: arXiv:2002.04461. doi: 10.48550/arXiv.2002.04461.

99. V. Bergen, M. Lange, S. Peidli, F. A. Wolf, and F. J. Theis, “Generalizing RNA velocity to transient cell states through dynamical modeling,” Nat Biotechnol, vol. 38, no. 12, Art. no. 12, Dec. 2020

100. G. La Manno et al., “RNA velocity of single cells,” Nature, vol. 560, no. 7719, Art. no. 7719, Aug. 2018

101. L. Haghverdi, M. Büttner, F. A. Wolf, F. Buettner, and F. J. Theis, “Diffusion pseudotime robustly reconstructs lineage branching,” Nat Methods, vol. 13, no. 10, Art. no. 10, Oct. 2016

102. Cui, Haotian, et al. "Towards multimodal foundation models in molecular cell biology." Nature 640.8059 (2025): 623-633.

103. Heumos, Lukas, et al. "Pertpy: an end-to-end framework for perturbation analysis." Nature Methods (2025): 1-10. 

104. Yang, Karren Dai, et al. "Multi-domain translation between single-cell imaging and sequencing data using autoencoders." Nature communications 12.1 (2021): 31.

OTHER RESOURCES

Graph Attention Networks: https://petar-v.com/GAT/

NVIDIA  and other related sites, etc...