| [1] |
Addala V, Newell F, Pearson JV, Redwood A, Robinson BW, Creaney J, et al. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol 2024; 21(1):28-46.
|
| [2] |
Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 2024; 21(8):1470-1480.
|
| [3] |
Tran NH, Qiao R, Xin L, Chen X, Shan B, Li M. Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines. Nat Mach Intell 2020; 2(12):764-771.
|
| [4] |
Cao LL, Kagan JC. Targeting innate immune pathways for cancer immunotherapy. Immunity 2023; 56(10):2206-2217.
|
| [5] |
Esteva A, Feng J, van D der Wal, Huang SC, Simko JP, DeVries S, et al. Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. NPJ Digit Med 2022; 5(1):71.
|
| [6] |
He D, Liu Q, Wu Y, Xie L. A context–aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening. Nat Mach Intell 2022; 4(10):879-892.
|
| [7] |
Pardi N, Krammer F. mRNA vaccines for infectious diseases—advances, challenges and opportunities. Nat Rev Drug Discov 2024; 23(11):838-861.
|
| [8] |
Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy. Cancer Cell 2023; 41(3):450-465.
|
| [9] |
Walsh LA, Daniela FQ. Decoding the tumor microenvironment with spatial technologies. Nat Immun 2023; 24(12):1982-1993.
|
| [10] |
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873):583-589.
|
| [11] |
Gao M, Nakajima D An, Parks JM, Skolnick J. AF2Complex predicts direct physical interactions in multimeric proteins with deep learning. Nat Commun 2022; 13:1744.
|
| [12] |
Brunton SL, Kutz JN. Promising directions of machine learning for partial differential equations. Nat Comput Sci 2024; 4(7):483-494.
|
| [13] |
Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng 2023; 7(6):719-742.
|
| [14] |
Yan S, Yu Z, Zhang X, Mahapatra D, Chandra SS, Janda M, et al. Towards trustable skin cancer diagnosis via rewriting model’s decision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2023 Jun 18–22; Vancouver, BC, Canada; 2023.
|
| [15] |
DeGrave AJ, Cai ZR, Janizek JD, Daneshjou R, Lee SI. Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians. Nat Biomed Eng. In press.
|
| [16] |
Perez-Lopez R, Ghaffari LN, Mahmood F, Kather JN. A guide to artificial intelligence for cancer researchers. Nat Rev Cancer 2024; 24(6):427-441.
|
| [17] |
Viswanathan VS, Parmar V, Madabhushi A. Towards equitable AI in oncology. Nat Rev Clin Oncol 2024; 21(8):628-637.
|
| [18] |
Mihir M, Christopher SP, Melissa JM. Tailor made: the art of therapeutic mRNA design. Nat Rev Drug Discov 2024; 23(1):67-83.
|
| [19] |
Ingraham JB, Baranov M, Costello Z, Barber KW, Wang W, Ismail A, et al. Illuminating protein space with a programmable generative model. Nature 2023; 623(7989):1070-1078.
|
| [20] |
Jayatunga MK, Xie W, Ruder L, Schulze U, Meier C. AI in smallmolecule drug discovery: a coming wave?. Nat Rev Drug Discov 2022; 21(3):175-176.
|
| [21] |
You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7(1):156.
|
| [22] |
Wang Y, Xia Y, Yan J, Yuan Y, Shen HB, Pan X. ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug–target interactions. Nat Commun 2023; 14:7861.
|
| [23] |
Kim Y, Eddins A, Anand S, Wei KX, van E den Berg, Rosenblatt S, et al. Evidence for the utility of quantum computing before fault tolerance. Nature 2023; 618(7965):500-505.
|
| [24] |
Li J, Topaloglu RO, Ghosh S. Quantum generative models for small molecule drug discovery. IEEE Trans Quantum Eng 2021; 2:3103308.
|
| [25] |
Li W, Yin Z, Li X, Ma D, Yi S, Zhang Z, et al. A hybrid quantum computing pipeline for real world drug discovery. Sci Rep 2024; 14:16942.
|
| [26] |
Kraehenbuehl L, Weng CH, Eghbali S, Wolchok JD, Merghoub T. Enhancing immunotherapy in cancer by targeting emerging immunomodulatory pathways. Nat Rev Clin Oncol 2022; 19(1):37-50.
|
| [27] |
Carreras J, Roncador G, Hamoudi R. Artificial intelligence predicted overall survival and classified mature B-cell neoplasms based on immuno-oncology and immune checkpoint panels. Cancers 2022; 14(21):5318.
|
| [28] |
Kong J, Ha D, Lee J, Kim I, Park M, Im SH, et al. Network-based machine learning approach to predict immunotherapy response in cancer patients. Nat Commun 2022; 13:3703.
|
| [29] |
Huang Q, Dong X, Zhang P, Wang B, He C, Wang J, et al. OPERA: alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2024 Jun 17–21; Seattle, WA, USA; 2024.
|
| [30] |
Farquhar S, Kossen J, Kuhn L, Gal Y. Detecting hallucinations in large language models using semantic entropy. Nature 2024; 630(8017):625-630.
|
| [31] |
Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv Neural Inf Process Syst 2020; 33:9459-9474.
|
| [32] |
Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, et al. Chain-of-thought prompting elicits reasoning in large language models. In: Proceedings of the Thirty-Sixth Conference on Neural Information Processing Systems; 2022 Nov 28–Dec 9; New Orleans, LA, USA; 2022.
|
| [33] |
Jeong M, Sohn J, Sung M, Kang J. Improving medical reasoning through retrieval and self-reflection with retrieval-augmented large language models. Bioinformatics 2024; 40(Suppl 1):i119-i129.
|
| [34] |
Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. Nature 2023; 620(7972):172-180.
|
| [35] |
Sorin M, Rezanejad M, Karimi E, Fiset B, Desharnais L, Perus LJM, et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature 2023; 614(7948):548-554.
|
| [36] |
Kiemen AL, Braxton AM, Grahn MP, Han KS, Babu JM, Reichel R, et al. CODA: quantitative 3D reconstruction of large tissues at cellular resolution. Nat Methods 2022; 19(11):1490-1499.
|
| [37] |
McDonald TO, Cheng YC, Graser C, Nicol PB, Temko D, Michor F. Computational approaches to modelling and optimizing cancer treatment. Nat Rev Bioeng 2023; 1(10):695-711.
|
| [38] |
Yuan C, Ryan PB, Ta C, Guo Y, Li Z, Hardin J, et al. Criteria2Query: a natural language interface to clinical databases for cohort definition. J Am Med Inform Assoc 2019; 26(4):294-305.
|
| [39] |
Lu L, Dercle L, Zhao B, Schwartz LH. Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging. Nat Commun 2021; 12:6654.
|
| [40] |
Dolezal JM, Wolk R, Hieromnimon HM, Howard FM, Srisuwananukorn A, Karpeyev D, et al. Deep learning generates synthetic cancer histology for explainability and education. NPJ Precis Oncol 2023; 7(1):49.
|
PDF(774 KB)
