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Salcedo A, Tarabichi M, Buchanan A, Espiritu S, Zhang H, Zhu K Ou-Yang T-H, Leshchiner I, Anastassiou D, Guan Y, GH Jang, Hasse K, Deshwar A, Zou W, Umar I, Dentro S, Wintersinger J, Chiotti K, Demeulemeester J, Jolly C, Scyza L, Ko M, Wedge D. Morris Q, Ellrot K, Van Loo P, Mooter MFE, Boutros, P. In press. Crowd-sourced benchmarking of single-sample tumour subclonal reconstruction. Nature Biotechnology. 2024 Jun 11. doi: 10.1038/s41587-024-02250-y

Zhang H, Kreis J, Schelhorn SE, Dahmen H, Grombacher T, Zühlsdorf M, Frank T. Zenke FT, Guan Y. 2023. Mapping combinatorial drug effects to DNA damage response kinase inhibitors. Nature Communications. 14, Article number: 8310 (2023)

Li H, Guan Y.  2022. Asymmetric Predictive Relationships Across Histone Modifications. Nature Machine Intelligence. 4, pages 288–299

Guan Y, Li H, Yi D, Zhang D, Yin C, Li K, Zhang P. 2021. A survival model generalized to regression learning algorithms. Nature Computational Science. https://doi.org/10.1038/s43588-021-00083-2

Lu J, Bender B, Jin J, Guan Y. 2021. Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modeling. Nature Machine Intelligence. https://doi.org/10.1038/s42256-021-00357-4

Xiao Y, Wang X, Zhang H, Ulintz P, Li H, Guan Y. 2020. FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples. Nature Communications. 2020 Sep 8;11(1):4469. doi: 10.1038/s41467-020-18169-2.

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Guan Y.  2019.  Waking up to data challenges. Nature Machine Intelligence. Jan 7 1 (1), 67

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Li D, Balamurugan S, Yang YF, Zhang JW, Huang D, Zou LG, Yang WD, Liu JS, Guan Y*, Li HY*.  2019.  Transcriptional regulation of microalgae for concurrent lipid overproduction and secretion. Science Advances. Jan 30;5(1):eaau3795. doi: 10.1126/sciadv.aau3795. eCollection 2019 Jan. *Co-senior

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Sieberts SK, Zhu F, García-García J, Stahl E, Pratap A, Pandey G, Pappas D, Aguilar D, Anton B, Bonet J, Eksi R, Fornés O, Guney E, Li H, Marín MA, Panwar B, Planas-Iglesias J, Poglayen D, Cui J, Falcao AO, Suver C, Hoff B, Balagurusamy VSK, Dillenberger D, Neto EC, Norman T, Aittokallio T, Ammad-Ud-Din M, Azencott CA, Bellón V, Boeva V, Bunte K, Chheda H, Cheng L, Corander J, Dumontier M, Goldenberg A, Gopalacharyulu P, Hajiloo M, Hidru D, Jaiswal A, Kaski S, Khalfaoui B, Khan SA, Kramer ER, Marttinen P, Mezlini AM, Molparia B, Pirinen M, Saarela J, Samwald M, Stoven V, Tang H, Tang J, Torkamani A, Vert JP, Wang B, Wang T, Wennerberg K, Wineinger NE, Xiao G, Xie Y, Yeung R, Zhan X, Zhao C; Members of the Rheumatoid Arthritis Challenge Consortium, Greenberg J, Kremer J, Michaud K, Barton A, Coenen M, Mariette X, Miceli C, Shadick N, Weinblatt M, de Vries N, Tak PP, Gerlag D, Huizinga TWJ, Kurreeman F, Allaart CF, Louis Bridges S Jr, Criswell L, Moreland L, Klareskog L, Saevarsdottir S, Padyukov L, Gregersen PK, Friend S, Plenge R, Stolovitzky G, Oliva B, Guan Y, Mangravite LM.  2016.  Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications. 7:12460.