Department of Medical Statistics, University Goettingen

Dr. Mohammed Dakna


Research Assistent - Core Facility for Medical Biometry and Statistical Bioinformatics
Phone:
0551-39-12270
Telefax:
0551-39-4995
E-Mail:
Mohammed.Dakna@med.uni-goettingen.de
Office room:
Humboldtallee 32, GF, 164
ORCID iD:
 orcid.org//0000-0001-8341-0360

Research Interests
  1. Analysis of Mass spectrometry data generated in proteomics and metabolomics research
  2. Medical decision making using combinations of clinical data and new high dimensional molecular biomarkers signatures
  3. Statistical analysis of data with missing values
  4. Information theory

Brief CV:

[since   2016] Research assistant at the Core Facility for Medical and Statistical Bioinformatics,
Göttingen
[2006 - 2016]Head of Biostatistics and Bioinformatics, Mosaiques-Diagnostics GmbH,
Hannover
[2004 - 2005] Physics and mathematics lecturer, Adult education
Göttingen
[1999 - 2004] Research assistant at the theoretical physics institut
Göttingen
[1995 - 1999] PhD studies and research assistant in quantum information processing
and quantum state engineering, Jena [Here are two papers that were
part of my PhD thesis and have been cited more than 100 times
in the journals of the APS (American Physical Society) Paper1 and Paper2 ]
[1992 - 1995]Diploma studies and research assistant in high energy physics,
Dortmund
[1986 - 1991] Liscens-es-science in physics and chemistry from
the university Abdelmalek Essadi, Tetouan (Morrocco) [Link]

Selected publications (Medicine/Bioinformatics/Biostatistics)

[1] Delatola, E. and Dakna, M. (2018). Statistical Inference in High-Dimensional Omics Data. Integration of Omics Approaches and Systems Biology for Clinical Applications (John Wiley & Sons, Inc.), 196--206.

[2] Pejchinovski, M., Siwy, J., Metzger, J., Dakna, M., Mischak, H., Klein, J., Jankowski, V., Bae, K. T., Chapman, A. B., and Kistler, A. D. (2017). Urine peptidome analysis predicts risk of end-stage renal disease and reveals proteolytic pathways involved in autosomal dominant polycystic kidney disease progression. Nephrol. Dial. Transplant., 32(3):487--497.

[3] Carrick, E., Vanmassenhove, J., Glorieux, G., Metzger, J., Dakna, M., Pejchinovski, M., Jankowski, V., Mansoorian, B., Husi, H., Mullen, W., Mischak, H., Vanholder, R., and Van Biesen, W. (2016). Development of a MALDI MS-based platform for early detection of acute kidney injury. Proteomics Clin Appl, 10(7):732--742.

[4] Schonemeier, B., Metzger, J., Klein, J., Husi, H., Bremer, B., Armbrecht, N., Dakna, M., Schanstra, J. P., Rosendahl, J., Wiegand, J., Jager, M., Mullen, W., Breuil, B., Plentz, R. R., Lichtinghagen, R., Brand, K., Kuhnel, F., Mischak, H., Manns, M. P., and Lankisch, T. O. (2016). Urinary Peptide Analysis Differentiates Pancreatic Cancer From Chronic Pancreatitis. Pancreas, 45(7):1018--1026.

[5] Frantzi, M., van Kessel, K. E., Zwarthoff, E. C., Marquez, M., Rava, M., Malats, N., Merseburger, A. S., Katafigiotis, I., Stravodimos, K., Mullen, W., Zoidakis, J., Makridakis, M., Pejchinovski, M., Critselis, E., Lichtinghagen, R., Brand, K., Dakna, M., Roubelakis, M. G., Theodorescu, D., Vlahou, A., Mischak, H., and Anagnou, N. P. (2016). Development and Validation of Urine-based Peptide Biomarker Panels for Detecting Bladder Cancer in a Multi-center Study. Clin. Cancer Res., 22(16):4077--4086.

[6] Bhat, A., Dakna, M., and Mischak, H. (2015). Integrating proteomics profiling data sets: a network perspective. Methods Mol Biol, 1243:237--53.

[7] Gleiss, A., Dakna, M., Mischak, H., and Heinze, G. (2015). Two-group comparisons of zero-inflated intensity values: the choice of test statistic matters. Bioinformatics, 31(14):2310--7.

[8] Schanstra, J. P., Zurbig, P., Alkhalaf, A., Argiles, A., Bakker, S. J., Beige, J., Bilo, H. J., Chatzikyrkou, C., Dakna, M., Dawson, J., Delles, C., Haller, H., Haubitz, M., Husi, H., Jankowski, J., Jerums, G., Kleefstra, N., Kuznetsova, T., Maahs, D. M., Menne, J., Mullen, W., Ortiz, A., Persson, F., Rossing, P., Ruggenenti, P., Rychlik, I., Serra, A. L., Siwy, J., Snell-Bergeon, J., Spasovski, G., Staessen, J. A., Vlahou, A., Mischak, H., and Vanholder, R. (2015). Diagnosis and prediction of ckd progression by assessment of urinary peptides. J Am Soc Nephrol, 26(8):1999--2010.

[9] Nkuipou-Kenfack, E., Bhat, A., Klein, J., Jankowski, V., Mullen, W., Vlahou, A., Dakna, M., Koeck, T., Schanstra, J. P., Zurbig, P., Rudolph, K. L., Schumacher, B., Pich, A., and Mischak, H. (2015). Identification of ageing-associated naturally occurring peptides in human urine. Oncotarget, 6(33):34106--34117.

[10] Chinello, C., Cazzaniga, M., De Sio, G., Smith, A. J., Gianazza, E., Grasso, A., Rocco, F., Signorini, S., Grasso, M., Bosari, S., Zoppis, I., Dakna, M., van der Burgt, Y. E., Mauri, G., and Magni, F. (2014). Urinary signatures of renal cell carcinoma investigated by peptidomic approaches. PLoS One, 9(9):e106684.

[11] Frantzi, M., Metzger, J., Banks, R. E., Husi, H., Klein, J., Dakna, M., Mullen, W., Cartledge, J. J., Schanstra, J. P., Brand, K., Kuczyk, M. A., Mischak, H., Vlahou, A., Theodorescu, D., and Merseburger, A. S. (2014). Discovery and validation of urinary biomarkers for detection of renal cell carcinoma. J Proteomics, 98:44--58.

[12] Nkuipou-Kenfack, E., Duranton, F., Gayrard, N., Argiles, A., Lundin, U., Weinberger, K. M., Dakna, M., Delles, C., Mullen, W., Husi, H., Klein, J., Koeck, T., Zurbig, P., and Mischak, H. (2014). Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease. PLoS One, 9(5):e96955.

[13] Stalmach, A., Johnsson, H., McInnes, I. B., Husi, H., Klein, J., Dakna, M., Mullen, W., Mischak, H., and Porter, D. (2014). Identification of urinary peptide biomarkers associated with rheumatoid arthritis. PLoS One, 9(8):e104625.

[14] Argiles, A., Siwy, J., Duranton, F., Gayrard, N., Dakna, M., Lundin, U., Osaba, L., Delles, C., Mourad, G., Weinberger, K. M., and Mischak, H. (2013). Ckd273, a new proteomics classifier assessing ckd and its prognosis. PLoS One, 8(5):e62837.

[15] Klein, J., Lacroix, C., Caubet, C., Siwy, J., Zurbig, P., Dakna, M., Muller, F., Breuil, B., Stalmach, A., Mullen, W., Mischak, H., Bandin, F., Monsarrat, B., Bascands, J. L., Decramer, S., and Schanstra, J. P. (2013). Fetal urinary peptides to predict postnatal outcome of renal disease in fetuses with posterior urethral valves (puv). Sci Transl Med, 5(198):198ra106.

[16] Metzger, J., Negm, A. A., Plentz, R. R., Weismuller, T. J., Wedemeyer, J., Karlsen, T. H., Dakna, M., Mullen, W., Mischak, H., Manns, M. P., and Lankisch, T. O. (2013). Urine proteomic analysis differentiates cholangiocarcinoma from primary sclerosing cholangitis and other benign biliary disorders. Gut, 62(1):122--30.

[17] Molin, L., Seraglia, R., Lapolla, A., Ragazzi, E., Gonzalez, J., Vlahou, A., Schanstra, J. P., Albalat, A., Dakna, M., Siwy, J., Jankowski, J., Bitsika, V., Mischak, H., Zurbig, P., and Traldi, P. (2012). A comparison between maldi-ms and ce-ms data for biomarker assessment in chronic kidney diseases. J Proteomics, 75(18):5888--97.

[18] Dakna, M., Harris, K., Kalousis, A., Carpentier, S., Kolch, W., Schanstra, J. P., Haubitz, M., Vlahou, A., Mischak, H., and Girolami, M. (2010). Addressing the challenge of defining valid proteomic biomarkers and classifiers. BMC Bioinformatics, 11:594.

[19] Good, D. M., Zurbig, P., Argiles, A., Bauer, H. W., Behrens, G., Coon, J. J., Dakna, M., Decramer, S., Delles, C., Dominiczak, A. F., Ehrich, J. H., Eitner, F., Fliser, D., Frommberger, M., Ganser, A., Girolami, M. A., Golovko, I., Gwinner, W., Haubitz, M., Herget-Rosenthal, S., Jankowski, J., Jahn, H., Jerums, G., Julian, B. A., Kellmann, M., Kliem, V., Kolch, W., Krolewski, A. S., Luppi, M., Massy, Z., Melter, M., Neususs, C., Novak, J., Peter, K., Rossing, K., Rupprecht, H., Schanstra, J. P., Schiffer, E., Stolzenburg, J. U., Tarnow, L., Theodorescu, D., Thongboonkerd, V., Vanholder, R., Weissinger, E. M., Mischak, H., and Schmitt-Kopplin, P. (2010). Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics, 9(11):2424--37.

[20] Mischak, H., Allmaier, G., Apweiler, R., Attwood, T., Baumann, M., Benigni, A., Bennett, S. E., Bischoff, R., Bongcam-Rudloff, E., Capasso, G., Coon, J. J., D'Haese, P., Dominiczak, A. F., Dakna, M., Dihazi, H., Ehrich, J. H., Fernandez-Llama, P., Fliser, D., Frokiaer, J., Garin, J., Girolami, M., Hancock, W. S., Haubitz, M., Hochstrasser, D., Holman, R. R., Ioannidis, J. P., Jankowski, J., Julian, B. A., Klein, J. B., Kolch, W., Luider, T., Massy, Z., Mattes, W. B., Molina, F., Monsarrat, B., Novak, J., Peter, K., Rossing, P., Sanchez-Carbayo, M., Schanstra, J. P., Semmes, O. J., Spasovski, G., Theodorescu, D., Thongboonkerd, V., Vanholder, R., Veenstra, T. D., Weissinger, E., Yamamoto, T., and Vlahou, A. (2010). Recommendations for biomarker identification and qualification in clinical proteomics. Sci Transl Med, 2(46):46ps42.

[21] Dakna, M., He, Z., Yu, W. C., Mischak, H., and Kolch, W. (2009). Technical, bioinformatical and statistical aspects of liquid chromatography-mass spectrometry (lc-ms) and capillary electrophoresis-mass spectrometry (ce-ms) based clinical proteomics: a critical assessment. J Chromatogr B Analyt Technol Biomed Life Sci, 877(13):1250--8.

[22] Haubitz, M., Good, D. M., Woywodt, A., Haller, H., Rupprecht, H., Theodorescu, D., Dakna, M., Coon, J. J., and Mischak, H. (2009). Identification and validation of urinary biomarkers for differential diagnosis and evaluation of therapeutic intervention in anti-neutrophil cytoplasmic antibody-associated vasculitis. Mol Cell Proteomics, 8(10):2296--307.

[23] Kistler, A. D., Mischak, H., Poster, D., Dakna, M., Wuthrich, R. P., and Serra, A. L. (2009). Identification of a unique urinary biomarker profile in patients with autosomal dominant polycystic kidney disease. Kidney Int, 76(1):89--96.

[24] Zurbig, P., Decramer, S., Dakna, M., Jantos, J., Good, D. M., Coon, J. J., Bandin, F., Mischak, H., Bascands, J. L., and Schanstra, J. P. (2009). The human urinary proteome reveals high similarity between kidney aging and chronic kidney disease. Proteomics, 9(8):2108--17.

[25] Coon, J. J., Zurbig, P., Dakna, M., Dominiczak, A. F., Decramer, S., Fliser, D., Frommberger, M., Golovko, I., Good, D. M., Herget-Rosenthal, S., Jankowski, J., Julian, B. A., Kellmann, M., Kolch, W., Massy, Z., Novak, J., Rossing, K., Schanstra, J. P., Schiffer, E., Theodorescu, D., Vanholder, R., Weissinger, E. M., Mischak, H., and Schmitt-Kopplin, P. (2008). Ce-ms analysis of the human urinary proteome for biomarker discovery and disease diagnostics. Proteomics Clin Appl, 2(7-8):964.

[26] Rossing, K., Mischak, H., Dakna, M., Zurbig, P., Novak, J., Julian, B. A., Good, D. M., Coon, J. J., Tarnow, L., and Rossing, P. (2008). Urinary proteomics in diabetes and ckd. J Am Soc Nephrol, 19(7):1283--90.