Publications

In the oven

2021

2020

2019

  • CRISPR, single cells, machine learning - what more do you want?
    Network method Review Data generation and network reconstruction strategies for single cell transcriptomic profiles of CRISPR-mediated gene perturbations
    A.N. Holding, H.V. Cook, F. Markowetz
    Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, Nov 2019
    PMID:31756390 | doi:10.1016/j.bbagrm.2019.194441
  • ...
    Cancer tissue Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging
    Li C, Wang S, Yan JL, Torheim T, Boonzaier NR, Sinha R, Matys T, Markowetz F, Price SJ
    J Neurosurg. 2019 Apr 26:1-8.
    PMID:31026822 | doi:10.3171/2018.12.JNS182926
  • Master Regulator Analysis for ChIP-seq data
    Network method VULCAN integrates ChIP-seq with patient-derived co-expression networks to identify GRHL2 as a key co-regulator of ERa at enhancers in breast cancer
    A.N. Holding*, F.M. Giorgi*, A. Donnelly, A.E Cullen, L.A. Selth, F. Markowetz
    Genome Biology, 2019 20:91
    PMID:31084623 | bioRxiv:266908 | doi:10.1186/s13059-019-1698-z
  • ISMB 2019
    Cancer genome Estimating the predictability of cancer evolution
    SR Hosseini, R Diaz-Uriarte, F Markowetz, N Beerenwinkel
    Bioinformatics, 35(14), July 2019, Pages i389–i397
    PMID:31510665 | doi:10.1093/bioinformatics/btz332
  • Adaptive immune response co-evolves with metastatic genomes
    Cancer genome The genomic and immune landscapes of lethal metastatic breast cancer
    L De Mattos-Arruda*, S-J Sammut*, E.M. Ross, R. Bashford-Rogers, E. Greenstein, H. Markus, S. Morganella, Y. Teng, Y. Maruvka, B. Pereira, O.M. Rueda, S-F Chin, T. Contente-Cuomo, R. Mayor, A. Arias, H.R. Ali, W. Cope, D. Tiezzi, D. Reshef, N. Ciriaco, E. Martinez-Saez, V. Peg, S. Ramon y Cajal, J. Cortes, G. Vassiliou, G. Getz, S. Nik-Zainal, M. Murtaza, N. Friedman, F. Markowetz, J. Seoane^, Carlos Caldas^
    Cell Reports, 27(9), P2690-2708.e10, May 28, 2019
    PMID:31141692 | doi:10.1016/j.celrep.2019.04.098
  • Immune differences even if there are no genomic differences
    Cancer genome Immuno-phenotypes of Pancreatic Ductal Adenocarcinoma: Metaanalysis of transcriptional subtypes
    I. de Santiago, C. Yau, M. Middleton, M. Dustin, F. Markowetz, S. Sivakumar
    International Journal of Cancer, 2019 Feb 5
    PMID:30720864 | doi:10.1002/ijc.32186 | bioRxiv:182261
  • Habitats in the brain
    Cancer tissue Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging: correlation with patient survival
    C. Li, J-L Yan, T. Torheim, M.A. McLean, N.R. Boonzaier, Y. Huang, B.R.J. Van Dijken, T. Matys, F. Markowetz, S.J. Price
    Radiotherapy and Oncology, 2019 May;134:17-24.
    PMID:31005212 | bioRxiv:180521 | doi:10.1101/180521
  • Cancer tissue Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals
    C. Li, S. Wang, P. Liu, T. Torheim, N.R. Boonzaier, B.R. van Dijken, C.B. Schönlieb, F. Markowetz, S.J. Price
    Neoplasia. 2019 Mar 31;21(5):442-449.
    PMID:30943446 | doi:10.1016/j.neo.2019.03.005
  • Radiomics in brain cancer
    Cancer tissue Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma
    C. Li, S. Wang, A. Serra, T. Torheim, J-L. Yan, N.R. Boonzaier, Y. Huang, T. Matys, M.A. McLean, F. Markowetz, S.J. Price
    European Radiology, 2019 Feb 1
    PMID:30707277 | doi:10.1007/s00330-018-5984-z

2018

  • Challenging the central dogma of ER biology
    Network method Genome-wide Estrogen Receptor-alpha activation is sustained, not cyclical
    A.N. Holding, A.E. Cullen, F. Markowetz
    eLife, 2018;7:e40854
    PMID:30457555 | doi:10.7554/eLife.40854 | bioRxiv:398925
  • We hate to contradict you, but ...
    Cancer genome Contradictory results Neutral tumor evolution?
    M. Tarabichi, I. Martincorena, M. Gerstung, F. Markowetz, PT Spellman, QD Morris, OC Lingjaerde, DC Wedge, P. Van Loo
    Nature Genetics, Nov 2018
    PMID:30374075 | bioRxiv:158006 | doi:10.1101/158006
  • Heterogeneity in the brain
    Cancer tissue Intratumoral heterogeneity of tumor infiltration of glioblastoma revealed by joint histogram analysis of diffusion tensor imaging
    C. Li, S. Wang, J-L Yan, R.J. Piper, H. Liu, T. Torheim, H. Kim, N.R. Boonzaier, R. Sinha, T. Matys, F. Markowetz, S.J. Price
    Neurosurgery, 2018 Sep 17 nyy388
    PMID:30239840 | doi:10.1093/neuros/nyy388 | bioRxiv:187450
  • clinical impact of intra-tumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations
    Cancer tissue Intra-tumor heterogeneity defines treatment-resistant HER2+ breast tumors.
    I.H. Rye*, A. Trinh*, A. Satersdal, D. Nebdal, OC. Lingjaerde, V. Almendro, K. Polyak, A-L. Borresen-Dale, A. Helland, F. Markowetz^, H. Russnes^
    Molecular Oncology, 2018 Aug 22
    PMID:30133130 | doi:10.1002/1878-0261.12375 | bioRxiv:297549
  • A serendipitous metabolic target for glioblastoma
    Cancer genome The small molecule KHS101 induces bioenergetic dysfunction in glioblastoma cells through inhibition of mitochondrial HSPD1
    E.S. Polson, V.B. Kuchler, C. Abbosh, E.M. Ross, R.K. Mathew, H.A. Beard, B. Da Silva, A.N. Holding, S. Ballereau, E. Chuntharpursat-Bon, J. Williams, H.B.S. Griffiths, H Shao, A. Patel, A.J. Davies, A. Droop, P. Chumas, S.C. Short, M. Lorger, J.E. Gestwicki, L.D. Roberts, R.S. Bon, S.J. Allison, S. Zhu, F. Markowetz, H. Wurdak
    Science Translational Medicine, 2018 Aug 15;10(454). pii: eaar2718
    PMID:30111643 | doi:10.1126/scitranslmed.aar2718 | bioRxiv:205203
  • Deconstructing the mutational forces shaping the complex genomes of ovarian cancers
    Cancer genome Copy-number signatures and mutational processes in ovarian carcinoma
    G. Macintyre*, T. Goranova*, D. De Silva, D. Ennis, A.M. Piskorz, M. Eldridge, D. Sie, L-A. Lewsley, A. Hanif, C. Wilson, S. Dowson, R.M. Glasspool, M. Lockley, E. Brockbank, A. Montes, A. Walther, S. Sundar, R. Edmondson, G.D. Hall, A. Clamp, C. Gourley, M. Hall, C. Fotopoulou, H. Gabra, J. Paul, A. Supernat, D. Millan, A. Hoyle, G. Bryson, C. Nourse, L. Mincarelli, L. Navarro Sanchez, B. Ylstra, M. Jimenez-Linan, L. Moore, O. Hofmann, F. Markowetz^, I.A. McNeish^, J.D. Brenton^
    Nature Genetics, 50, 1262–1270 (2018)
    PMID:30104763 | doi:10.1038/s41588-018-0179-8 | bioRxiv:174201
  • Quantitative RIME
    Cancer genome A quantitative mass spectrometry-based approach to monitor the dynamics of endogenous chromatin-associated protein complexes
    E Papachristou, K Kishore, A Holding, K Harvey, T Roumeliotis, C Chilamakuri, S Omarjee, KM Chia, A Swarbrick, E Lim, F Markowetz, M Eldridge, R Siersbaek, C D'Santos, J Carroll
    Nature Communications, 2018 Jun 13;9(1):2311.
    PMID:29899353 | doi:10.1038/s41467-018-04619-5
  • Peak height matters.
    Cancer genome Parallel factor ChIP provides essential internal control for quantitative differential ChIP-seq
    M.J. Guertin, A.E. Cullen, F. Markowetz, A.N. Holding
    Nucleic Acid Research, Volume 46, Issue 12, 6 July 2018, Pages e75
    PMID:29672735 | doi:10.1093/nar/gky252 | bioRxiv:182261
  • Inferring a network from data on nodes, edges and paths
    Network method Estimating cellular pathways from an ensemble of heterogeneous data sources
    A.M. Franks, F. Markowetz^, E.M. Airoldi^
    Annals of Applied Statistics, 2018, Vol. 12, No. 3, 1361-1384.
    doi:10.1214/16-AOAS915 | arXiv:1406.5799

2017

  • Tissue heterogeneity predicts progression
    Cancer tissue Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma.
    TC Booth, TJ Larkin, Y Yuan, MI Kettunen, SN Dawson, D Scoffings, HC Canuto, SL Vowler, H Kirschenlohr, MP Hobson, F Markowetz, S Jefferies, KM Brindle
    PLoS ONE, 2017 May 17;12(5):e0176528
    PMID:28520730 | doi:10.1371/journal.pone.0176528
  • What's downstream of a genetic interaction?
    Network biology Inferring modulators of genetic interactions with epistatic Nested Effects Models
    M. Pirkl*, M. Diekmann*, M. van der Wees, H. Fröhlich, N. Beerenwinkel, F. Markowetz
    PLoS Comput Biol, 2017; 13(4):e1005496.
    PMID:28406896 | doi:10.1371/journal.pcbi.1005496
  • My contribution to the 'Research Matters' collection
    Opinion All biology is computational biology
    F. Markowetz
    PLoS Biology, 2017 Mar 9;15(3):e2002050.
    PMID:28278152 | doi:10.1371/journal.pbio.2002050
  • Rigorous removal of biases from ChIP-seq data
    Cancer genome BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes
    I. de Santiago*, W. Liu*, K. Yuan, M. O'Reilly, CSR Chilamakuri, BAJ Ponder, KB Meyer^, F. Markowetz^
    Genome Biology, 2017 Feb 24;18(1):39
    PMID:28235418 | doi:10.1186/s13059-017-1165-7 | bioRxiv:093393
  • Biology and subtypes, we got it all.
    Network biology Master regulators of oncogenic KRAS response in pancreatic cancer: an integrative network biology analysis
    S. Sivakumar*, I. de Santiago*, L. Chlon*, F. Markowetz
    PLOS Medicine, 2017 Jan 31;14(1):e1002223.
    PMID:28141826 | doi:10.1371/journal.pmed.1002223
  • Diverse and surprising metastatic patterns and how to spot them
    Cancer genome Review How subclonal modelling is changing the metastatic paradigm
    G. Macintyre, P. Van Loo, N.M. Corcoran, D.C. Wedge, F. Markowetz^, C.M. Hovens^
    Clinical Cancer Research, 2017 Feb 1;23(3):630-635
    PMID:27864419 | doi:10.1158/1078-0432.CCR-16-0234
  • IHC-based classifier to validate the prognostic and predictive value of molecular CRC subtyping
    Cancer tissue Practical and robust identification of molecular subtypes in colorectal cancer by immunohistochemistry
    A. Trinh, K. Trumpi, F. De Sousa E Melo, X. Wang, J.H. de Jong, E. Fessler, P.J.K. Kuppen, M.S. Reimers, M. Swets, M. Koopman, I.D. Nagtegaal, M. Jansen, G.K.J. Hooijer, G.J.A. Offerhaus, O. Kranenburg, C.J. Punt, J.P. Medema, F. Markowetz, L. Vermeulen
    Clinical Cancer Research 2017 Jan 15;23(2):387-398
    PMID:27459899 | doi:10.1158/1078-0432.CCR-16-0680

2016

  • Cancer tissue Patterns of Immune Infiltration in Breast Cancer and their Clinical Implications: A Gene Expression-Based Retrospective Study
    R. Ali, L. Chlon, P. Pharoah, F. Markowetz, C. Caldas
    PLOS Medicine, 2016 Dec 13;13(12):e1002194
    PMID:27959923 | doi:10.1371/journal.pmed.1002194
  • CLOE can capture both the acquisition and the loss of mutations, as well as episodes of convergent evolution
    Cancer genome A phylogenetic latent feature model for clonal deconvolution
    F. Marass, F. Mouliere, K. Yuan, N. Rosenfeld, F. Markowetz
    Annals of Applied Statistics, Volume 10, Number 4 (2016), 2377-2404.
    doi:10.1214/16-AOAS986 | arXiv:1604.01715
  • News and Views
    Cancer genome News and Views A saltationist theory of cancer evolution
    F. Markowetz
    Nature Genetics, 48(10), 1102–1103 (2016)
    PMID:27681287 | doi:10.1038/ng.3687 | full text access
  • Phylogenetic inference tailored to the size and noise of current single cell data
    Cancer genome OncoNEM: Inferring tumour evolution from single-cell sequencing data
    E.M. Ross and F. Markowetz
    Genome Biology 2016, 17:69
    PMID:27083415 | doi:10.1186/s13059-016-0929-9
  • Accumulated metabolites of hydroxybutyric acid serve as diagnostic and prognostic biomarkers of high-grade serous ovarian carcinomas
    M. Hilvo, I. de Santiago, P. Gopalacharyulu, W.D. Schmitt, J. Budczies, M. Kuhberg, M. Dietel, T. Aittokallio, F. Markowetz, C. Denkert, J. Sehouli, C. Frezza, S. Darb-Esfahani, I. Braicu
    Cancer Research, 2016 Feb 15;76(4):796-804
    PMID:26685161 | doi:10.1158/0008-5472.CAN-15-2298
  • How do germline and somatic genetic events combine to influence cancer development?
    Cancer genome Network method Regulators of genetic risk of breast cancer identified by integrative network analysis
    M.A.A. Castro, I. de Santiago, T.M. Campbell, C. Vaughn, T.E. Hickey, E. Ross, W.D. Tilley, F. Markowetz, B.A.J. Ponder and K.B. Meyer
    Nature Genetics, 2016 Jan;48(1):12-21
    PMID:26618344 | doi:10.1038/ng.3458

2015

  • Ask what reproducibility can do for you!
    blog
    Comment Five selfish reasons to work reproducibly
    F. Markowetz
    Genome Biology 2015, 16:274
    PMID:26646147 | doi:10.1186/s13059-015-0850-7
  • My contribution to the 'About my lab' collection
    Opinion You are not working for me; I am working with you
    F. Markowetz
    PLoS Comput Biol 2015, 11(9): e1004387.
    PMID:26402330 | doi:10.1371/journal.pcbi.1004387
  • Software: BitPhylogeny
    Cancer genome BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies
    K. Yuan*, T. Sakoparnig*, F. Markowetz^, N. Beerenwinkel^
    Genome Biology, 2015, 16:36
    PMID:25786108 | doi:10.1186/s13059-015-0592-6
  • Heterogeneity goes translational!
    PLoS Med Editorial
    Press release
    F1000
    Cancer genome Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction
    R.F. Schwarz, C.K.Y. Ng, S.L. Cooke, S. Newman, J. Temple, A.M. Piskorz, D. Gale, K. Sayal, M. Murtaza, P.J Baldwin, N. Rosenfeld, H.M. Earl, E. Sala, M. Jimenez-Linan, C.A. Parkinson, F. Markowetz^, J.D. Brenton^
    PLoS Medicine, 2015 Feb 24;12(2):e1001789.
    PMID:25710373 | doi:10.1371/journal.pmed.1001789
  • The first book to comprehensively cover the field of systems genetics. Systems Genetics Cover Systems Genetics - linking genotypes and phenotypes
    Florian Markowetz and Michael Boutros (eds.)
    Cambridge University Press
    July 2015
    more info
     
    Including as chapters:
    An Introduction to Systems Genetics. F Markowetz and M Boutros [ free PDF ]
    Joining the dots - network analysis of gene perturbation data. X Wang, K Yuan, F Markowetz
  • Everything you ever wanted to know about tumour evolution but were too afraid to ask.
    Cancer genome Review Cancer evolution: mathematical models and computational inference
    N. Beerenwinkel, R.F. Schwarz, M. Gerstung, F. Markowetz
    Systematic Biology, 2015 Jan;64(1):e1-25.
    PMID:25293804 | doi:10.1093/sysbio/syu081

2014

  • highly accessed
    Images + genomics = awesome!
    GB research highlight
    Press release
    Cancer tissue Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss as a common driver event and prognostic classifier
    F.C. Martins*, I. de Santiago*, A. Trinh*, J. Xian, A. Guo, K. Sayal, M. Jimenez-Linan, S. Deen, K. Driver, M. Mack, J. Aslop, P.D. Pharoah, F. Markowetz^, J.D. Brenton^
    Genome Biology, 2014, 15:526
    PMID:25608477 | doi:10.1186/s13059-014-0526-8
  • Software: GoIFISH
    Cancer tissue GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images
    A. Trinh, I.H. Rye, V. Almendro, A. Helland, H.G. Russnes^, F. Markowetz^
    Genome Biology, 2014, 15:442
    PMID:25168174 | doi:10.1186/s13059-014-0442-y
  • Rigorous implementation of an intuitive measure of functional association.
    Software: SANTA
    Network method SANTA: quantifying the functional content of molecular networks
    A. Cornish, F. Markowetz
    PLoS Comp Bio 2014, 10(9):e1003808.
    PMID:25210953 | doi:10.1371/journal.pcbi.1003808 | arXiv:1407.4658
  • F1000
    Software: MEDICC
    Cancer genome Phylogenetic quantification of intra-tumour heterogeneity
    R.F. Schwarz, A. Trinh, B. Sipos, J.D. Brenton, N. Goldman, F. Markowetz
    PLoS Comp Bio 2014, 10(4): e1003535
    PMID:24743184 | doi:10.1371/journal.pcbi.1003535 | arXiv:1306.1685
  • HM-NEM = HMM + NEM
    Network method Reconstructing evolving signaling networks by Hidden Markov Nested Effects Models
    X. Wang, K. Yuan, C. Hellmayr, W. Liu, F. Markowetz
    Annals of Applied Statistics, Volume 8, Number 1 (2014), 1-647
    doi:10.1214/13-AOAS696 | PDF

2013

  • Review article
    Cancer genome Review Dissecting cancer heterogeneity - an unsupervised classification approach
    X. Wang, F. Markowetz, F.D. Melo, J.P. Medema, L. Vermeulen
    The International Journal of Biochemistry & Cell Biology pii: S1357-2725(13)00282-3
    PMID:24004832 | doi:10.1016/j.biocel.2013.08.014
  • Mechanisms behind FGFR2, the breast cancer GWAS top hit
    Cancer genome Master regulators of FGFR2 signalling and breast cancer risk
    M.N.C. Fletcher*, M.A.A. Castro*, X. Wang, I. de Santiago, M. O'Reilly, S-F. Chin, O.M. Rueda, C. Caldas, B.A.J. Ponder, F Markowetz^, K.B. Meyer^
    Nature Communications 4:2464 (2013)
    PMID:24043118 | doi:10.1038/ncomms3464
  • F1000
     
    Data: DeSouza2013
     
    More here:
    Nature Rev Cancer
    Nature Rev Clin Onc
    Nature Rev Gastro
    Cancer genome Poor prognosis colon cancer is defined by a distinct molecular subtype and develops from serrated precursor lesions
    F. De Sousa Mello*, X. Wang*, M. Jansen, E. Fessler, A. Trinh, L.P. de Rooij, J.H. de Jong, O.J. de Boer, R. van Leersum, M.F. Bijlsma, H Rodermond, M. van der Heijden, C.J. van Noesel, J.B. Tuynman, E. Dekker, F. Markowetz, J.P. Medema^, L. Vermeulen^
    Nature Medicine, 2013 May;19(5):614-8.
    PMID:23584090 | doi:10.1038/nm.3174

2012

  • Software: CRImage
    Data: EGAS00000000098
    Reproducibility:
    Sweave
    Data and code in R
     
    More here:
    Nature Outlook 2013
    GenomeWeb
    CRUK press release
    Cancer tissue Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling
    Y. Yuan, H. Failmezger, O.M. Rueda, H.R. Ali, S. Gräf, S-F. Chin, R.F. Schwarz, C Curtis, M.J. Dunning, H. Bardwell, N. Johnson, S. Doyle, G. Turashvili, E. Provenzano, S. Aparicio, C. Caldas, F. Markowetz
    Science Translational Medicine, 4, 157ra142 (2012)
    PMID:23100629 | doi:10.1126/scitranslmed.3004330
  • Companion paper to Mulder et al, Nat Cell Bio 2012.
    Software: PANR
    Network method Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations
    X. Wang, M.A. Castro, K.W. Mulder^, F. Markowetz^
    PLoS Comp Bio 8(6): e1002566
    PMID:22761558 | doi:10.1371/journal.pcbi.1002566
  • see also Wang et al, PLoS Comp Bio 2012
    Data: Mulder2012
     
    More here
    News and Views
    Nature Cell Biology Cover Diverse epigenetic strategies interact to control epidermal differentiation
    K.W. Mulder, X. Wang, C. Escriu, Y. Ito, R.F. Schwarz, J. Gillis, G. Sirokmany, G. Donati, S. Uribe-Lewis, P. Pavlidis, A. Murrell, F. Markowetz, F. Watt
    Nature Cell Biology 14(7), 753-763 (2012)
    PMID:22729083 | doi:10.1038/ncb2520
  • highly accessed
    Network Data Integr- ation, Analysis, and Visualization
    Software: RedeR
    Network method RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations
    M.A. Castro, X. Wang, M.N.C. Fletcher, K.B. Meyer, F. Markowetz
    Genome Biology 2012, 13:R29
    PMID:22531049 | doi:10.1186/gb-2012-13-4-r29
  • Data: EGAS00000000083
     
    More here:
    Nature Rev Cancer
    Cancer Discovery
    BBC News
    ABC News @ youtube
    CRUK video @ youtube
    Cancer genome The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
    C. Curtis*, S.P. Shah*, S.-F. Chin*, G. Turashvili*, O.M. Rueda, M.J. Dunning, D. Speed, A.G. Lynch, S. Samarajiwa, Y. Yuan, S. Gräf, G. Ha, G. Haffari, A. Bashashati, R. Russell, S. McKinney, METABRIC Group, A. Langerød, A. Green, E. Provenzano, G. Wishart, S. Pinder, P. Watson, F. Markowetz, L. Murphy, I. Ellis, A. Purushotham, A.-L. Børresen-Dale, J.D. Brenton, S. Tavaré, C. Caldas^ and S. Aparicio^
    Nature 486, 7403 (2012)
    PMID:22522925 | doi:10.1038/nature10983

2011

  • For each patient we check concordance of signals in different data when assigning them to clusters.
    Software: PSDF
    Cancer genome Patient-specific data fusion defines prognostic cancer subtypes
    Y. Yuan*, R. S. Savage*, F. Markowetz
    PLoS Comp Bio 7(10): e1002227
    PMID:22028636 | doi:10.1371/journal.pcbi.1002227
  • Software: DANCE
    Network method Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer
    Y. Yuan, O. M. Rueda, C. Curtis, F. Markowetz
    Bioinformatics. 2011 Oct 1;27(19):2679-85.
    PMID:21804112 | doi:10.1186/10.1093/bioinformatics/btr450
  • Network method Differential C3NET reveals disease networks of direct physical interactions
    G. Altay, M. Asim, F. Markowetz, D. E. Neal
    BMC Bioinformatics 2011, 12:296
    PMID:21777411 | doi:10.1186/1471-2105-12-296
  • Software: lol
     
    Conference version won best paper award at IEEE BIBM 2010
    Network method A sparse regulatory network of copy-number driven gene expression reveals putative breast cancer oncogenes
    Y. Yuan, C. Curtis, C. Caldas, F. Markowetz
    IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul 20
    PMID:21788678 | doi:10.1109/TCBB.2011.105 | arXiv:1010.1409
  • Software: joda
    Network method Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data
    E. Szczurek, F. Markowetz, Irit Gat-Viks, P. Biecek, J. Tiuryn, M. Vingron
    BMC Bioinformatics 2011, 12:249
    PMID:21693013 | doi:10.1186/1471-2105-12-249
  • Software: HTSanalyzeR
    Network method HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens
    X. Wang*, C. Terfve*, J.C. Rose, F. Markowetz
    Bioinformatics (2011) 27 (6): 879-880
    PMID:21258062 | doi:10.1093/bioinformatics/btr028

2010

  • Machine Learning meets evolution: Finite state transducer beat comp- etitors in phylogenetic inference.
    Network method Evolutionary distances in the twilight zone - a rational kernel approach
    R. F. Schwarz, W. Fletcher, F. Förster, B. Merget, M. Wolf, J. Schultz, F. Markowetz
    PLoS One, 2010 Dec 31;5(12):e15788
    PMID:21209825 | doi:10.1371/journal.pone.0015788 | arXiv:1011.5096
  • How predictive is histone acetylation for gene expression over time? With a focus on key stem cell genes.
    Mapping dynamic histone acetylation patterns to gene expression in Nanog-depleted Murine embryonic stem cells
    F. Markowetz, K.W. Mulder, E.M. Airoldi, I.R. Lemischka, O.G. Troyanskaya
    PLoS Comp Bio, 2010 Dec 16;6(12):e1001034
    PMID:21187909 | doi:10.1371/journal.pcbi.1001034 | arXiv:1010.3268
  • Constructing networks from single gene pert- urbations. Review based on ISMB 2009 and 2010 tutorials.
    Network method Review How to understand the cell by breaking it: network analysis of gene perturbation screens
    F. Markowetz
    PLoS Comp Bio, 2010 Feb 26;6(2):e1000655.
    PMID:20195495 | doi:10.1371/journal.pcbi.1000655 | arXiv:0910.2938

2009

  • F1000
    News and Views: Kim and Orkin, Nat BT 2010
    • Editor's choice Sci. Sig. 2(98) 2009

    Systems-level dynamic analyses of fate change in murine embryonic stem cells
    R. Lu, F. Markowetz, R.D. Unwin, J.T. Leek, E.M. Airoldi, B.D. MacArthur, A. Lachmann, R. Rozov, A. Ma'ayan, L.A. Boyer, O.G. Troyanskaya, A.D. Wetton, I.R. Lemischka.
    Nature. 2009 Nov 19;462(7271):358-362
    PMID:19924215 | doi:10.1038/nature08575

2008

  • The 'nem' R-package unites software from several labs Network method Analyzing Gene Perturbation Screens With Nested Effects Models in R and Bioconductor
    H. Fröhlich, T. Beißbarth, A. Tresch, D. Kostka, J. Jacob, R. Spang, F. Markowetz
    Bioinformatics, 2008 Nov 1;24(21):2549-50.
    PMID:18718939 | doi:10.1093/bioinformatics/btn446
  • An alternative and extended formulation of NEMs including feature selection Network method Structure Learning in Nested Effects Models
    A. Tresch, F. Markowetz.
    Stat Appl in Gen and Mol Bio (SAGMB): Vol. 7: Iss. 1, Article 9, 2008.
    PMID:18312214 | doi:10.2202/1544-6115.1332 | arXiv:0710.4481
  • Cancer genome Book chapter Computational diagnostics with gene expression profiles
    C. Lottaz, D. Kostka, F. Markowetz, R. Spang.
    Methods Mol Biol. 2008;453:281-96.
    PMID:18712310 | doi:10.1007/978-1-60327-429-6

2007

  • Divide-and-conquer approach to efficiently infer NEMs Network method Nested Effects Models for High-Dimensional Phenotyping Screens
    F. Markowetz, D. Kostka, O.G. Troyanskaya, R. Spang.
    Bioinformatics 2007 23(13):i305-i312
    PMID:17646311 | doi:10.1093/bioinformatics/btm178
  • highly accessed
    Comprehensive description of statistical network inference
    Network method Review Inferring cellular networks - a review
    F. Markowetz, R. Spang.
    BMC Bioinformatics, 8(Suppl 6):S5, 2007
    PMID:17903286 | doi:10.1186/1471-2105-8-S6-S5
  • Network method Review Computational identification of cellular networks and pathways
    F. Markowetz, O.G. Troyanskaya.
    Molecular BioSystems, 3(7):478-482, 2007
    PMID:17579773 | doi:10.1039/b617014p

2006

  • Cancer genome Book chapter Computational Diagnostics
    R. Spang and F. Markowetz
    In: D. Ganten and K. Ruckpaul (eds.), Encyclopedic Reference of Genomics and Proteomics in Molecular Medicine, Springer, 2006. ISBN: 3-540-44244-8
    book website

2005

  • Nested Effects Models (NEMs) introduced Network method Non-transcriptional Pathway Features Reconstructed from Secondary Effects of RNA Interference
    F. Markowetz, J. Bloch, R. Spang.
    Bioinformatics 2005 21: 4026-4032.
    PMID:16159925 | doi:10.1093/bioinformatics/bti662
  • Extending statistical perturbation models from knock-outs to knock-downs Network method Probabilistic Soft Interventions in Conditional Gaussian Networks
    F. Markowetz, S. Grossmann, R. Spang.
    Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS), Barbados, 2005
    pdf
  • Cancer genome Molecular Diagnosis: Classification, Model Selection, and Performance Evaluation
    F. Markowetz and R. Spang.
    Methods of Information in Medicine, 44(3): 438-43, 2005
    PMID:16113770 | pdf

2003

  • Support Vector Machines for Protein Fold Class Prediction
    F. Markowetz, L. Edler, M. Vingron.
    Biometrical Journal 45 (2003) 3, 377-389
    doi:10.1002/bimj.200390019
  • Acetylcysteine for Prevention of Contrast Nephropathy: Meta-analysis
    R. Birck, S. Krzossok, F. Markowetz, P. Schnülle, F. J. v. d. Woude and C. Braun.
    Lancet. 2003 Aug 23; 362(9384): 598-603.
    PMID:12944058 | doi:10.1016/S0140-6736(03)14189-X
  • Network method Evaluating the Effect of Perturbations in Reconstructing Network Topologies
    F. Markowetz and R. Spang.
    Proc. of the 3rd Int Workshop on Distributed Statistical Computing, Vienna, Austria, 2003
    pdf

2002

  • Cancer genome Class Discovery in Gene Expression Data: Characterizing Splits by Support Vector Machines
    F. Markowetz and A. von Heydebreck.
    In Proc. of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., Springer 2002.
    pdf