Parreno, V. et al. Transient lack of polycomb parts induces an epigenetic most cancers destiny. Nature 629, 688–696 (2024).
Google Scholar
Brown, R., Curry, E., Magnani, L., Wilhelm-Benartzi, C. S. & Borley, J. Poised epigenetic states and purchased drug resistance in most cancers. Nat. Rev. Most cancers 14, 747–753 (2014).
Google Scholar
Adduri, A. Okay. et al. Predicting mobile responses to perturbation throughout various contexts with State. Preprint at bioRxiv (2025).
Zhao, S. et al. SToFM: a multi-scale basis mannequin for spatial transcriptomics. Preprint at (2025).
Bunne, C. et al. Tips on how to construct the digital cell with synthetic intelligence: priorities and alternatives. Cell 187, 7045–7063 (2024).
Google Scholar
Salvador-Barbero, B. et al. CDK4/6 inhibitors impair restoration from cytotoxic chemotherapy in pancreatic adenocarcinoma. Most cancers Cell 37, 340–353.e346 (2020).
Google Scholar
Fang, Y. et al. Sequential remedy with PARP and WEE1 inhibitors minimizes toxicity whereas sustaining efficacy. Most cancers Cell 35, 851–867.e857 (2019).
Google Scholar
Lee, M. J. et al. Sequential utility of anticancer medication enhances cell dying by rewiring apoptotic signaling networks. Cell 149, 780–794 (2012).
Google Scholar
Goldman, A. et al. Temporally sequenced anticancer medication overcome adaptive resistance by concentrating on a weak chemotherapy-induced phenotypic transition. Nat. Commun. 6, 6139 (2015).
Google Scholar
Liu, Z. et al. A proteomic and phosphoproteomic panorama of KRAS mutant cancers identifies mixture therapies. Mol. Cell 81, 4076–4090.e4078 (2021).
Google Scholar
Wang, L., Lankhorst, L. & Bernards, R. Exploiting senescence for the therapy of most cancers. Nat. Rev. Most cancers 22, 340–355 (2022).
Google Scholar
Wang, C. et al. Inducing and exploiting vulnerabilities for the therapy of liver most cancers. Nature 574, 268–272 (2019).
Google Scholar
Li, F. et al. Blocking methionine catabolism induces senescence and confers vulnerability to GSK3 inhibition in liver most cancers. Nat. Most cancers 5, 131–146 (2024).
Silver, D. et al. Mastering the sport of Go along with deep neural networks and tree search. Nature 529, 484–489 (2016).
Google Scholar
Baek, M. et al. Correct prediction of protein constructions and interactions utilizing a three-track neural community. Science 373, 871–876 (2021).
Google Scholar
Wang, G. et al. Optimized glycemic management of kind 2 diabetes with reinforcement studying: a proof-of-concept trial. Nat. Med. 29, 2633–2642 (2023).
Google Scholar
Yala, A. et al. Optimizing risk-based breast most cancers screening insurance policies with reinforcement studying. Nat. Med. 28, 136–143 (2022).
Google Scholar
Zhang, H. et al. Algorithm for optimized mRNA design improves stability and immunogenicity. Nature 621, 396–403 (2023).
Google Scholar
Weaver, D. T., King, E. S., Maltas, J. & Scott, J. G. Reinforcement studying informs optimum therapy methods to restrict antibiotic resistance. Proc. Natl Acad. Sci. USA 121, e2303165121 (2024).
Google Scholar
Jo, Okay., Sung, I., Lee, D., Jang, H. & Kim, S. Inferring transcriptomic cell states and transitions solely from time collection transcriptome information. Sci. Rep. 11, 12566 (2021).
Google Scholar
Rockne, R. C. et al. State-transition evaluation of time-sequential gene expression identifies essential factors that predict improvement of acute myeloid leukemia. Most cancers Res. 80, 3157–3169 (2020).
Google Scholar
Mnih, V. et al. Human-level management by deep reinforcement studying. Nature 518, 529–533 (2015).
Google Scholar
Lotfollahi, M. et al. Predicting mobile responses to complicated perturbations in high-throughput screens. Mol. Syst. Biol. 19, e11517 (2023).
Google Scholar
Huang, W. & Liu, H. Predicting single-cell mobile responses to perturbations utilizing cycle consistency studying. Bioinformatics 40, i462–i470 (2024).
Google Scholar
Piran, Z., Cohen, N., Hoshen, Y. & Nitzan, M. Disentanglement of single-cell information with biolord. Nat. Biotechnol. 42, 1678–1683 (2024).
Google Scholar
Qi, X. et al. Predicting transcriptional responses to novel chemical perturbations utilizing deep generative mannequin for drug discovery. Nat. Commun. 15, 9256 (2024).
Google Scholar
Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nat. Strategies 16, 715–721 (2019).
Google Scholar
Szalai, B. et al. Signatures of cell dying and proliferation in perturbation transcriptomics information—from confounding issue to efficient prediction. Nucleic Acids Res. 47, 10010–10026 (2019).
Google Scholar
Patwardhan, G. A. et al. Therapy scheduling results on the evolution of drug resistance in heterogeneous most cancers cell populations. NPJ Breast Most cancers 7, 60 (2021).
Google Scholar
Johnson, T. I. et al. Quantifying cell cycle-dependent drug sensitivities in most cancers utilizing a excessive throughput synchronisation and screening method. EBioMedicine 68, 103396 (2021).
Google Scholar
Vijayaraghavalu, S., Dermawan, J. Okay., Cheriyath, V. & Labhasetwar, V. Extremely synergistic impact of sequential therapy with epigenetic and anticancer medication to beat drug resistance in breast most cancers cells is mediated by way of activation of p21 gene expression resulting in G2/M cycle arrest. Mol. Pharm. 10, 337–352 (2013).
Google Scholar
Easwaran, H., Tsai, H.-C. & Baylin, S. B. Most cancers epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol. Cell 54, 716–727 (2014).
Google Scholar
Bloom, S. I., Islam, M. T., Lesniewski, L. A. & Donato, A. J. Mechanisms and penalties of endothelial cell senescence. Nat. Rev. Cardiol. 20, 38–51 (2023).
Google Scholar
Sutton, R. S. & Barto, A. G. Reinforcement Studying: An Introduction (MIT Press, 2018).
Subramanian, A. et al. A subsequent technology connectivity map: L1000 platform and the primary 1,000,000 profiles. Cell 171, 1437–1452. e1417 (2017).
Google Scholar
Barrett, T. et al. NCBI GEO: archive for purposeful genomics information units—replace. Nucleic Acids Res. 41, D991–D995 (2012).
Google Scholar
RDKit: Open-Supply Cheminformatics; https://www.rdkit.org
Enache, O. M. et al. The GCTx format and cmap {Py, R, M, J} packages: sources for optimized storage and built-in traversal of annotated dense matrices. Bioinformatics 35, 1427–1429 (2019).
Google Scholar
Kingma, D. P. & Ba, J. Adam: a technique for stochastic optimization. Preprint at (2014).
Prechelt, L. in Neural Networks: Methods of the Commerce 55–69 (Springer, 2002).
Paszke, A. et al. PyTorch: an crucial type, high-performance deep studying library. In thirty third Convention on Neural Info Processing Methods (NeurIPS 2019) (2019).
Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Most cancers Discov. 5, 1210–1223 (2015).
Google Scholar
Pedregosa, F. et al. Scikit-learn: machine studying in Python. J. Mach. Be taught. Res. 12, 2825–2830 (2011).
Google Scholar
Brownlee, J. A delicate introduction to the rectified linear unit (ReLU). Mach. Be taught. Mastery 6 (2019).
Yang, L., Liu, S., Tsoka, S. & Papageorgiou, L. G. Mathematical programming for piecewise linear regression evaluation. Knowledgeable Syst. Appl. 44, 156–167 (2016).
Google Scholar
Wagenmakers, E.-J. & Farrell, S. AIC mannequin choice utilizing Akaike weights. Psychon. Bull. Rev. 11, 192–196 (2004).
Google Scholar
Wu, T. et al. clusterProfiler 4.0: a common enrichment device for decoding omics information. Innovation 2, 100141 (2021).
Wishart, D. S. et al. DrugBank 5.0: a significant replace to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).
Google Scholar
Virtanen, P. et al. SciPy 1.0: elementary algorithms for scientific computing in Python. Nat. Strategies 17, 261–272 (2020).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Google Scholar
Dobin, A. & Gingeras, T. R. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinform. 51, 11.14. 11–11.14. 19 (2015).
Google Scholar
Li, B. & Dewey, C. N. RSEM: correct transcript quantification from RNA-seq information with or and not using a reference genome. BMC Bioinf. 12, 323 (2011).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq information with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
Brockman, G. et al. OpenAI Fitness center. Preprint at (2016).
Weng, J. et al. Tianshou: a extremely modularized deep reinforcement studying library. J. Mach. Be taught. Res. 23, 1–6 (2022).
Google Scholar



