Publications

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. In press.


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.


Salcedo A, Tarabichi M, Espiritu SMG, Deshwar AG, David M, Wilson NM, Dentro S, Wintersinger JA, Liu LY, Ko M, Sivanandan S, Zhang H, Zhu K, Ou Yang TH, Chilton JM, Buchanan A, Lalansingh CM, P'ng C, Anghel CV, Umar I, Lo B, Zou W; DREAM SMC-Het Participants, Simpson JT, Stuart JM, Anastassiou D, Guan Y, Ewing AD, Ellrott K, Wedge DC, Morris Q, Van Loo P, Boutros PC.  2020.  A community effort to create standards for evaluating tumor subclonal reconstruction. Nature Biotechnology. Jan;38(1):97-107. doi: 10.1038/s41587-019-0364-z. Epub 2020 Jan 9.


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


Menden MP, Wang D, Mason MJ, Szalai B, Bulusu KC, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M; AstraZeneca-Sanger Drug Combination DREAM Consortium, Jang IS, Ghazoui Z, Ahsen ME, Vogel R, Neto EC, Norman T, Tang EKY, Garnett MJ, Veroli GYD, Fawell S, Stolovitzky G, Guinney J, Dry JR, Saez-Rodriguez J.  2019.  Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications. (10, Article number: 2674 (2019))


Choobdar S, Ahsen ME, Crawford J, Tomasoni M, Fang T, Lamparter D, Lin J, Hescott B, Hu X, al. et.  2019.  Assessment of network module identification across complex diseases. Nature Methods. Sep;16(9):843-852. doi: 10.1038/s41592-019-0509-5. Epub 2019 Aug 30.


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


Keller A*, Gerkin RC*, Guan Y*, Dhurandhar A, Turu G, Szalai B, Mainland JD, Ihara Y, Yu CW, Wolfinger R, Vens C, Schietgat L, De Grave K, Norel R; DREAM Olfaction Prediction Consortium, Stolovitzky G, Cecchi GA, Vosshall LB, Meyer P.  2017.  Predicting human olfactory perception from chemical features of odor molecules. Science. Feb 20. pii: eaal2014. doi: 10.1126/science.aal2014. *Co-first


Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK, Graim K, Bivol A, Wang H, Zhu F, Afsari B, Danilova LV, Favorov AV, Lee WS, Taylor D, Hu CW, Long BL, Noren DP, Bisberg AJ; HPN-DREAM Consortium, Mills GB, Gray JW, Kellen M, Norman T, Friend S, Qutub AA, Fertig EJ, Guan Y, Song M, Stuart JM, Spellman PT, Koeppl H, Stolovitzky G, Saez-Rodriguez J, Mukherjee S.  2016.  Inferring causal molecular networks: empirical assessment through a community-based effort. Nature Methods. 13(4):310-8.


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.