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Items: 1 to 50 of 157

1.

Dysregulated mesenchymal PDGFR-β drives kidney fibrosis.

Buhl EM, Djudjaj S, Klinkhammer BM, Ermert K, Puelles VG, Lindenmeyer MT, Cohen CD, He C, Borkham-Kamphorst E, Weiskirchen R, Denecke B, Trairatphisan P, Saez-Rodriguez J, Huber TB, Olson LE, Floege J, Boor P.

EMBO Mol Med. 2020 Jan 14:e11021. doi: 10.15252/emmm.201911021. [Epub ahead of print]

2.

Quantitative Systems Toxicology Modeling To Address Key Safety Questions in Drug Development: A Focus of the TransQST Consortium.

Ferreira S, Fisher C, Furlong LI, Laplanche L, Park BK, Pin C, Saez-Rodriguez J, Trairatphisan P.

Chem Res Toxicol. 2020 Jan 21;33(1):7-9. doi: 10.1021/acs.chemrestox.9b00499. Epub 2020 Jan 7.

PMID:
31909603
3.

Bringing data from curated pathway resources to Cytoscape with OmniPath.

Ceccarelli F, Turei D, Gabor A, Saez-Rodriguez J.

Bioinformatics. 2019 Dec 30. pii: btz968. doi: 10.1093/bioinformatics/btz968. [Epub ahead of print]

PMID:
31886476
4.

Metabolic rewiring of the hypertensive kidney.

Rinschen MM, Palygin O, Guijas C, Palermo A, Palacio-Escat N, Domingo-Almenara X, Montenegro-Burke R, Saez-Rodriguez J, Staruschenko A, Siuzdak G.

Sci Signal. 2019 Dec 10;12(611). pii: eaax9760. doi: 10.1126/scisignal.aax9760.

PMID:
31822592
5.

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer.

Berndt N, Egners A, Mastrobuoni G, Vvedenskaya O, Fragoulis A, Dugourd A, Bulik S, Pietzke M, Bielow C, van Gassel R, Damink SWO, Erdem M, Saez-Rodriguez J, Holzhütter HG, Kempa S, Cramer T.

Br J Cancer. 2020 Jan;122(2):233-244. doi: 10.1038/s41416-019-0659-3. Epub 2019 Dec 10.

PMID:
31819186
6.

Influence of Liver Fibrosis on Lobular Zonation.

Ghallab A, Myllys M, Holland CH, Zaza A, Murad W, Hassan R, Ahmed YA, Abbas T, Abdelrahim EA, Schneider KM, Matz-Soja M, Reinders J, Gebhardt R, Berres ML, Hatting M, Drasdo D, Saez-Rodriguez J, Trautwein C, Hengstler JG.

Cells. 2019 Dec 2;8(12). pii: E1556. doi: 10.3390/cells8121556.

7.

The authors reply.

Saez-Rodriguez J, Rinschen MM, Floege J, Kramann R.

Kidney Int. 2019 Dec;96(6):1422-1423. doi: 10.1016/j.kint.2019.09.011. No abstract available.

PMID:
31759488
8.

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.

Guo T, Luna A, Rajapakse VN, Koh CC, Wu Z, Liu W, Sun Y, Gao H, Menden MP, Xu C, Calzone L, Martignetti L, Auwerx C, Buljan M, Banaei-Esfahani A, Ori A, Iskar M, Gillet L, Bi R, Zhang J, Zhang H, Yu C, Zhong Q, Varma S, Schmitt U, Qiu P, Zhang Q, Zhu Y, Wild PJ, Garnett MJ, Bork P, Beck M, Liu K, Saez-Rodriguez J, Elloumi F, Reinhold WC, Sander C, Pommier Y, Aebersold R.

iScience. 2019 Nov 22;21:664-680. doi: 10.1016/j.isci.2019.10.059. Epub 2019 Oct 31.

9.

From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL.

Liu A, Trairatphisan P, Gjerga E, Didangelos A, Barratt J, Saez-Rodriguez J.

NPJ Syst Biol Appl. 2019 Nov 11;5:40. doi: 10.1038/s41540-019-0118-z. eCollection 2019.

10.

Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders.

Manica M, Oskooei A, Born J, Subramanian V, Sáez-Rodríguez J, Rodríguez Martínez M.

Mol Pharm. 2019 Oct 31. doi: 10.1021/acs.molpharmaceut.9b00520. [Epub ahead of print]

PMID:
31618586
11.

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis.

Arnol D, Schapiro D, Bodenmiller B, Saez-Rodriguez J, Stegle O.

Cell Rep. 2019 Oct 1;29(1):202-211.e6. doi: 10.1016/j.celrep.2019.08.077.

12.

Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction.

Szalai B, Subramanian V, Holland CH, Alföldi R, Puskás LG, Saez-Rodriguez J.

Nucleic Acids Res. 2019 Nov 4;47(19):10010-10026. doi: 10.1093/nar/gkz805.

13.

Transfer of regulatory knowledge from human to mouse for functional genomics analysis.

Holland CH, Szalai B, Saez-Rodriguez J.

Biochim Biophys Acta Gene Regul Mech. 2019 Sep 13:194431. doi: 10.1016/j.bbagrm.2019.194431. [Epub ahead of print]

PMID:
31525460
14.

Reproducible biomedical benchmarking in the cloud: lessons from crowd-sourced data challenges.

Ellrott K, Buchanan A, Creason A, Mason M, Schaffter T, Hoff B, Eddy J, Chilton JM, Yu T, Stuart JM, Saez-Rodriguez J, Stolovitzky G, Boutros PC, Guinney J.

Genome Biol. 2019 Sep 10;20(1):195. doi: 10.1186/s13059-019-1794-0.

15.

Assessment of network module identification across complex diseases.

Choobdar S, Ahsen ME, Crawford J, Tomasoni M, Fang T, Lamparter D, Lin J, Hescott B, Hu X, Mercer J, Natoli T, Narayan R; DREAM Module Identification Challenge Consortium, Subramanian A, Zhang JD, Stolovitzky G, Kutalik Z, Lage K, Slonim DK, Saez-Rodriguez J, Cowen LJ, Bergmann S, Marbach D.

Nat Methods. 2019 Sep;16(9):843-852. doi: 10.1038/s41592-019-0509-5. Epub 2019 Aug 30.

16.

Elucidating essential kinases of endothelin signalling by logic modelling of phosphoproteomics data.

Schäfer A, Gjerga E, Welford RW, Renz I, Lehembre F, Groenen PM, Saez-Rodriguez J, Aebersold R, Gstaiger M.

Mol Syst Biol. 2019 Aug;15(8):e8828. doi: 10.15252/msb.20198828.

17.

Novel plasma peptide markers involved in the pathology of CKD identified using mass spectrometric approach.

Gajjala PR, Bruck H, Noels H, Heinze G, Ceccarelli F, Kribben A, Saez-Rodriguez J, Marx N, Zidek W, Jankowski J, Jankowski V.

J Mol Med (Berl). 2019 Oct;97(10):1451-1463. doi: 10.1007/s00109-019-01823-8. Epub 2019 Aug 5.

18.

Benchmark and integration of resources for the estimation of human transcription factor activities.

Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J.

Genome Res. 2019 Aug;29(8):1363-1375. doi: 10.1101/gr.240663.118. Epub 2019 Jul 24.

19.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.

Tang J, Gautam P, Gupta A, He L, Timonen S, Akimov Y, Wang W, Szwajda A, Jaiswal A, Turei D, Yadav B, Kankainen M, Saarela J, Saez-Rodriguez J, Wennerberg K, Aittokallio T.

NPJ Syst Biol Appl. 2019 Jul 8;5:20. doi: 10.1038/s41540-019-0098-z. eCollection 2019.

20.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

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.

Nat Commun. 2019 Jun 17;10(1):2674. doi: 10.1038/s41467-019-09799-2.

21.

Analysis of the Human Kinome and Phosphatome by Mass Cytometry Reveals Overexpression-Induced Effects on Cancer-Related Signaling.

Lun XK, Szklarczyk D, Gábor A, Dobberstein N, Zanotelli VRT, Saez-Rodriguez J, von Mering C, Bodenmiller B.

Mol Cell. 2019 Jun 6;74(5):1086-1102.e5. doi: 10.1016/j.molcel.2019.04.021. Epub 2019 May 14.

22.

Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR-Cas9 screening.

Picco G, Chen ED, Alonso LG, Behan FM, Gonçalves E, Bignell G, Matchan A, Fu B, Banerjee R, Anderson E, Butler A, Benes CH, McDermott U, Dow D, Iorio F, Stronach E, Yang F, Yusa K, Saez-Rodriguez J, Garnett MJ.

Nat Commun. 2019 May 16;10(1):2198. doi: 10.1038/s41467-019-09940-1.

23.

Author Correction: Linking drug target and pathway activation for effective therapy using multi-task learning.

Yang M, Simm J, Lam CC, Zakeri P, van Westen GJP, Moreau Y, Saez-Rodriguez J.

Sci Rep. 2019 May 3;9(1):7106. doi: 10.1038/s41598-019-43503-0.

24.

MEK1/2 inhibitor withdrawal reverses acquired resistance driven by BRAFV600E amplification whereas KRASG13D amplification promotes EMT-chemoresistance.

Sale MJ, Balmanno K, Saxena J, Ozono E, Wojdyla K, McIntyre RE, Gilley R, Woroniuk A, Howarth KD, Hughes G, Dry JR, Arends MJ, Caro P, Oxley D, Ashton S, Adams DJ, Saez-Rodriguez J, Smith PD, Cook SJ.

Nat Commun. 2019 May 2;10(1):2030. doi: 10.1038/s41467-019-09438-w.

25.

MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis.

Kotelnikova E, Kiani NA, Messinis D, Pertsovskaya I, Pliaka V, Bernardo-Faura M, Rinas M, Vila G, Zubizarreta I, Pulido-Valdeolivas I, Sakellaropoulos T, Faigle W, Silberberg G, Masso M, Stridh P, Behrens J, Olsson T, Martin R, Paul F, Alexopoulos LG, Saez-Rodriguez J, Tegner J, Villoslada P.

Proc Natl Acad Sci U S A. 2019 May 7;116(19):9671-9676. doi: 10.1073/pnas.1818347116. Epub 2019 Apr 19.

26.

Big science and big data in nephrology.

Saez-Rodriguez J, Rinschen MM, Floege J, Kramann R.

Kidney Int. 2019 Jun;95(6):1326-1337. doi: 10.1016/j.kint.2018.11.048. Epub 2019 Mar 5. Review.

27.

Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens.

Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, Migliardi G, Santos R, Rao Y, Sassi F, Pinnelli M, Ansari R, Harper S, Jackson DA, McRae R, Pooley R, Wilkinson P, van der Meer D, Dow D, Buser-Doepner C, Bertotti A, Trusolino L, Stronach EA, Saez-Rodriguez J, Yusa K, Garnett MJ.

Nature. 2019 Apr;568(7753):511-516. doi: 10.1038/s41586-019-1103-9. Epub 2019 Apr 10.

PMID:
30971826
28.

Elastin imaging enables noninvasive staging and treatment monitoring of kidney fibrosis.

Sun Q, Baues M, Klinkhammer BM, Ehling J, Djudjaj S, Drude NI, Daniel C, Amann K, Kramann R, Kim H, Saez-Rodriguez J, Weiskirchen R, Onthank DC, Botnar RM, Kiessling F, Floege J, Lammers T, Boor P.

Sci Transl Med. 2019 Apr 3;11(486). pii: eaat4865. doi: 10.1126/scitranslmed.aat4865.

PMID:
30944168
29.

Multi-omic measurements of heterogeneity in HeLa cells across laboratories.

Liu Y, Mi Y, Mueller T, Kreibich S, Williams EG, Van Drogen A, Borel C, Frank M, Germain PL, Bludau I, Mehnert M, Seifert M, Emmenlauer M, Sorg I, Bezrukov F, Bena FS, Zhou H, Dehio C, Testa G, Saez-Rodriguez J, Antonarakis SE, Hardt WD, Aebersold R.

Nat Biotechnol. 2019 Mar;37(3):314-322. doi: 10.1038/s41587-019-0037-y. Epub 2019 Feb 18.

PMID:
30778230
30.

The proteome microenvironment determines the protective effect of preconditioning in cisplatin-induced acute kidney injury.

Späth MR, Bartram MP, Palacio-Escat N, Hoyer KJR, Debes C, Demir F, Schroeter CB, Mandel AM, Grundmann F, Ciarimboli G, Beyer A, Kizhakkedathu JN, Brodesser S, Göbel H, Becker JU, Benzing T, Schermer B, Höhne M, Burst V, Saez-Rodriguez J, Huesgen PF, Müller RU, Rinschen MM.

Kidney Int. 2019 Feb;95(2):333-349. doi: 10.1016/j.kint.2018.08.037. Epub 2018 Dec 3.

PMID:
30522767
31.

Embracing the Dark Side: Computational Approaches to Unveil the Functionality of Genes Lacking Biological Annotation in Drug-Induced Liver Injury.

Souza T, Trairatphisan P, Piñero J, Furlong LI, Saez-Rodriguez J, Kleinjans J, Jennen D.

Front Genet. 2018 Nov 20;9:527. doi: 10.3389/fgene.2018.00527. eCollection 2018.

32.

Adipocyte-secreted BMP8b mediates adrenergic-induced remodeling of the neuro-vascular network in adipose tissue.

Pellegrinelli V, Peirce VJ, Howard L, Virtue S, Türei D, Senzacqua M, Frontini A, Dalley JW, Horton AR, Bidault G, Severi I, Whittle A, Rahmouni K, Saez-Rodriguez J, Cinti S, Davies AM, Vidal-Puig A.

Nat Commun. 2018 Nov 26;9(1):4974. doi: 10.1038/s41467-018-07453-x.

33.

Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data.

Razzaq M, Paulevé L, Siegel A, Saez-Rodriguez J, Bourdon J, Guziolowski C.

PLoS Comput Biol. 2018 Oct 29;14(10):e1006538. doi: 10.1371/journal.pcbi.1006538. eCollection 2018 Oct.

34.

In silico Prioritization of Transporter-Drug Relationships From Drug Sensitivity Screens.

César-Razquin A, Girardi E, Yang M, Brehme M, Saez-Rodriguez J, Superti-Furga G.

Front Pharmacol. 2018 Sep 7;9:1011. doi: 10.3389/fphar.2018.01011. eCollection 2018.

35.

The germline genetic component of drug sensitivity in cancer cell lines.

Menden MP, Casale FP, Stephan J, Bignell GR, Iorio F, McDermott U, Garnett MJ, Saez-Rodriguez J, Stegle O.

Nat Commun. 2018 Aug 23;9(1):3385. doi: 10.1038/s41467-018-05811-3.

36.

Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting.

Iorio F, Behan FM, Gonçalves E, Bhosle SG, Chen E, Shepherd R, Beaver C, Ansari R, Pooley R, Wilkinson P, Harper S, Butler AP, Stronach EA, Saez-Rodriguez J, Yusa K, Garnett MJ.

BMC Genomics. 2018 Aug 13;19(1):604. doi: 10.1186/s12864-018-4989-y.

37.

Gli1+ Mesenchymal Stromal Cells Are a Key Driver of Bone Marrow Fibrosis and an Important Cellular Therapeutic Target.

Schneider RK, Mullally A, Dugourd A, Peisker F, Hoogenboezem R, Van Strien PMH, Bindels EM, Heckl D, Büsche G, Fleck D, Müller-Newen G, Wongboonsin J, Ventura Ferreira M, Puelles VG, Saez-Rodriguez J, Ebert BL, Humphreys BD, Kramann R.

Cell Stem Cell. 2018 Aug 2;23(2):308-309. doi: 10.1016/j.stem.2018.07.006. No abstract available.

38.

How to find the right drug for each patient? Advances and challenges in pharmacogenomics.

Kalamara A, Tobalina L, Saez-Rodriguez J.

Curr Opin Syst Biol. 2018 Aug;10:53-62. doi: 10.1016/j.coisb.2018.07.001. Review.

39.

A microfluidics platform for combinatorial drug screening on cancer biopsies.

Eduati F, Utharala R, Madhavan D, Neumann UP, Longerich T, Cramer T, Saez-Rodriguez J, Merten CA.

Nat Commun. 2018 Jun 22;9(1):2434. doi: 10.1038/s41467-018-04919-w.

40.

Linking drug target and pathway activation for effective therapy using multi-task learning.

Yang M, Simm J, Lam CC, Zakeri P, van Westen GJP, Moreau Y, Saez-Rodriguez J.

Sci Rep. 2018 May 29;8(1):8322. doi: 10.1038/s41598-018-25947-y. Erratum in: Sci Rep. 2019 May 3;9(1):7106.

41.

Alternative models for sharing confidential biomedical data.

Guinney J, Saez-Rodriguez J.

Nat Biotechnol. 2018 May 9;36(5):391-392. doi: 10.1038/nbt.4128. No abstract available.

PMID:
29734317
42.

Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich.

Iorio F, Garcia-Alonso L, Brammeld JS, Martincorena I, Wille DR, McDermott U, Saez-Rodriguez J.

Sci Rep. 2018 Apr 30;8(1):6713. doi: 10.1038/s41598-018-25076-6.

43.

Whither systems medicine?

Apweiler R, Beissbarth T, Berthold MR, Blüthgen N, Burmeister Y, Dammann O, Deutsch A, Feuerhake F, Franke A, Hasenauer J, Hoffmann S, Höfer T, Jansen PL, Kaderali L, Klingmüller U, Koch I, Kohlbacher O, Kuepfer L, Lammert F, Maier D, Pfeifer N, Radde N, Rehm M, Roeder I, Saez-Rodriguez J, Sax U, Schmeck B, Schuppert A, Seilheimer B, Theis FJ, Vera J, Wolkenhauer O.

Exp Mol Med. 2018 Mar 2;50(3):e453. doi: 10.1038/emm.2017.290. Review.

44.

NADH Shuttling Couples Cytosolic Reductive Carboxylation of Glutamine with Glycolysis in Cells with Mitochondrial Dysfunction.

Gaude E, Schmidt C, Gammage PA, Dugourd A, Blacker T, Chew SP, Saez-Rodriguez J, O'Neill JS, Szabadkai G, Minczuk M, Frezza C.

Mol Cell. 2018 Feb 15;69(4):581-593.e7. doi: 10.1016/j.molcel.2018.01.034.

45.

Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells.

Wirbel J, Cutillas P, Saez-Rodriguez J.

Methods Mol Biol. 2018;1711:103-132. doi: 10.1007/978-1-4939-7493-1_6.

46.

Perturbation-response genes reveal signaling footprints in cancer gene expression.

Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J.

Nat Commun. 2018 Jan 2;9(1):20. doi: 10.1038/s41467-017-02391-6.

47.

A systematic atlas of chaperome deregulation topologies across the human cancer landscape.

Hadizadeh Esfahani A, Sverchkova A, Saez-Rodriguez J, Schuppert AA, Brehme M.

PLoS Comput Biol. 2018 Jan 2;14(1):e1005890. doi: 10.1371/journal.pcbi.1005890. eCollection 2018 Jan.

48.

Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer.

Garcia-Alonso L, Iorio F, Matchan A, Fonseca N, Jaaks P, Peat G, Pignatelli M, Falcone F, Benes CH, Dunham I, Bignell G, McDade SS, Garnett MJ, Saez-Rodriguez J.

Cancer Res. 2018 Feb 1;78(3):769-780. doi: 10.1158/0008-5472.CAN-17-1679. Epub 2017 Dec 11.

49.

Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines.

Blattmann P, Henriques D, Zimmermann M, Frommelt F, Sauer U, Saez-Rodriguez J, Aebersold R.

Cell Syst. 2017 Dec 27;5(6):604-619.e7. doi: 10.1016/j.cels.2017.11.002. Epub 2017 Dec 6.

50.

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells.

Gonçalves E, Sciacovelli M, Costa ASH, Tran MGB, Johnson TI, Machado D, Frezza C, Saez-Rodriguez J.

Metab Eng. 2018 Jan;45:149-157. doi: 10.1016/j.ymben.2017.11.011. Epub 2017 Nov 27.

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