Publications

Most papers are freely available (PDF links). Just ask me for the others.

Preprints

  • Watson, D. S. & Wright, M. N. (2019). Testing conditional independence in supervised learning algorithms. arXiv. https://arxiv.org/abs/1901.09917. PDF
  • Hüls, A., Wright, M. N., Bogl, L. H., Kaprio, J., Lissner, L., Molnár, D., Moreno, L., De Henauw, S., Siani, A., Veidebaum, T., Ahrens, W., Pigeot, I. & Foraita, R. on behalf of the IDEFICS/I.Family consortia (2020). A healthy childhood environment helps to combat inherited susceptibility to obesity. bioRxiv. https://doi.org/10.1101/2020.01.13.905125. PDF

Journal articles

  • Schmid, M., Welchowski T., Wright, M. N. & Berger, M. (2020). Discrete-time survival forests with Hellinger distance decision trees. Data Mining and Knowledge Discovery 34:812-832. https://doi.org/10.1007/s10618-020-00682-z. PDF
  • Weinhold, L., Schmid, M., Mitchell R., Maloney, K. O., Wright, M. N. & Berger, M. (2019). A random forest approach for modeling bounded outcome variables. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2019.1705310. Free Preprint
  • Hornung, R. & Wright, M. N. (2019). Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. https://doi.org/10.1186/s12859-019-2942-y. PDF
  • Steenbock, B., Wright, M. N., Wirsik, N. & Brandes, M. (2019). Accelerometry-based prediction of energy expenditure in preschoolers. Journal for the Measurement of Physical Behaviour 2:94-102. https://doi.org/10.1123/jmpb.2018-0032.
  • Boulesteix, A-L., Wright, M. N., Hoffmann, S. & König, I. R. (2019). Statistical learning approaches in the genetic epidemiology of complex diseases. Human Genetics 139:73–84. https://doi.org/10.1007/s00439-019-01996-9. Free read-only version
  • Wright, M. N. & König, I. R. (2019). Splitting on categorical predictors in random forests. PeerJ 7:e6339. https://doi.org/10.7717/peerj.6339. PDF
  • Probst, P., Wright, M. N. & Boulesteix, A-L. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1301. Free Preprint
  • Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M. & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518. https://doi.org/10.7717/peerj.5518. PDF
  • Fouodo, C. J. K., König, I. R., Weihs, C., Ziegler A. & Wright, M. N. (2018). Support vector machines for survival analysis with R. The R Journal 10:412–423. https://doi.org/10.32614/RJ-2018-005. PDF
  • Nembrini, S., König, I. R. & Wright, M. N. (2018). The revival of the Gini Importance? Bioinformatics 34:3711–3718. https://doi.org/10.1093/bioinformatics/bty373. PDF
  • Hirose, M., Schilf, P., Gupta, Y., Zarse, K., Künstner, A., Fähnrich, A., Busch, H., Yin, J., Wright, M. N., Ziegler, A., Vallier, M., Belheouane, M., Baines, J. F., Tautz, D., Johann, K., Oelkrug, R., Mittag, J., Lehnert, H., Othman, A., Jöhren, O., Schwaninger, M., Prehn, C., Adamski, J., Shima, K., Rupp, J., Häsler, R., Fuellen, G., Köhling, R., Ristow, M. & Ibrahim, S. M. (2018). Low-level mitochondrial heteroplasmy modulates DNA replication, glucose metabolism and lifespan in mice. Scientific Reports 8:5872. https://doi.org/10.1038/s41598-018-24290-6. PDF
  • Foraita, R., Dijkstra, L., Falkenberg, F., Garling, M., Linder, R., Pflock, R., Rizkallah, M. R., Schwaninger, M., Wright, M. N. & Pigeot, I. (2018). Detection of drug risks after approval: Methods development for the use of routine statutory health insurance data. Bundesgesundheitsblatt 61:1075–1081. https://doi.org/10.1007/s00103-018-2786-z. PDF
  • Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software 77:1–17. https://doi.org/10.18637/jss.v077.i01. PDF
  • Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruipérez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12:e0169748. https://doi.org/10.1371/journal.pone.0169748. PDF
  • Wright, M. N., Dankowski, T. & Ziegler, A. (2017). Unbiased split variable selection for random survival forests using maximally selected rank statistics. Statistics in Medicine 36:1272–1284. https://doi.org/10.1002/sim.7212. Free Preprint
  • Hirose, M., Schilf, P., Gupta, Y., Wright, M. N., Jöhren, O., Wagner, A. E., Sina, C., Ziegler, A., Ristow, M. & Ibrahim, S. M. (2016). Lifespan effects of mitochondrial mutations. Nature 540:E13–E14. https://doi.org/10.1038/nature20778.
  • Schmid, M., Wright, M. N. & Ziegler, A. (2016). On the use of Harrell’s C for clinical risk prediction via random survival forests. Expert Systems with Applications 63:450–459. https://doi.org/10.1016/j.eswa.2016.07.018. Free Preprint
  • Schirmer, J. H., Wright, M. N., Herrmann, K., Laudien, M., Nölle, B., Reinhold-Keller, E., Bremer, J. P., Moosig, F. & Holle, J. U. (2016). Myeloperoxidase-ANCA associated Granulomatosis with polyangiitis is a clinically distinct subset within ANCA-associated vasculitis. Arthritis & Rheumatology, 68:2953–2963. https://doi.org/10.1002/art.39786. PDF
  • Wright, M. N., Ziegler, A. & König, I. R. (2016). Do little interactions get lost in dark random forests? BMC Bioinformatics 17:145. https://doi.org/10.1186/s12859-016-0995-8. PDF
  • Schirmer, J. H., Wright, M. N., Vonthein, R., Herrmann, K., Nölle. B., Both, M., Henes, F., Arlt, A., Gross, W. L., Schinke, S., Reinhold-Keller, E., Moosig, F. & Holle, J. U. (2016). Clinical presentation and long-term outcome of 144 patients with microscopic polyangiitis in a monocentric German cohort. Rheumatology (Oxford) 55:71–79. https://doi.org/10.1093/rheumatology/kev286. PDF
  • Wright, M. N. & Ziegler, A. (2015). Multiple censored data in dentistry: A new statistical model for analyzing lesion size in randomized controlled trials. Biometrical Journal 57:384–394. https://doi.org/10.1002/bimj.201400118.
  • Paulick, C., Wright, M. N., Verleger, R. & Keller, K. (2014). Decomposition of 3-way arrays: A comparison of different PARAFAC algorithms. Chemometrics and Intelligent Laboratory Systems 137:97–109. https://doi.org/10.1016/j.chemolab.2014.06.009.

Book chapters

  • Wright, M. N.*, Gola D.*, Ziegler A. (2017) Preprocessing and Quality Control for Whole-Genome Sequences from the Illumina HiSeq X Platform. In: Elston R. C. (Ed.) Statistical Human Genetics (2nd edn.). Methods in Molecular Biology 1666:629-647. Humana Press, New York. https://doi.org/10.1007/978-1-4939-7274-6_30. HTML *Equal contribution