Publications

Stuff I have published

2017

  • M. Pettersson, M. Kierczak, M. S. Almén, S. Lamichhaney, and L. Andersson, “A Model-Free Approach For Detecting Genomic Regions Of Deep Divergence Using The Distribution Of Haplotype Distances,” bioRxiv, p. 144394, 2017.

  • M. Tatjewski, M. Kierczak, and D. Plewczynski, “Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices,” Prediction of Protein Secondary Structure, pp. 275-300, 2017.

2016

  • M. Olsson, M. Kierczak, Å. Karlsson, J. Jabło’nska, P. Leegwater, M. Koltookian, J. Abadie, D. C. De Citres, A. Thomas, Å. Hedhammar, and others, “Absolute quantification reveals the stable transmission of a high copy number variant linked to autoinflammatory disease,” BMC genomics, vol. 17, iss. 1, p. 299, 2016.

  • K. Tengvall, S. Kozyrev, M. Kierczak, K. Bergvall, F. H. Farias, B. Ardesjö-Lundgren, M. Olsson, E. Murén, R. Hagman, T. Leeb, and others, “Multiple regulatory variants located in cell type-specific enhancers within the PKP2 locus form major risk and protective haplotypes for canine atopic dermatitis in German shepherd dogs,” BMC genetics, vol. 17, iss. 1, p. 97, 2016.

  • K. Höglund, A-S. Lequarré, I. Ljungvall, K. Mc Entee, A-C. Merveille, M. Wiberg, V. Gouni, J. Lundgren Willesen, S. HanAas, G. Wess, and others, “Effect of breed on plasma endothelin-1 concentration, plasma renin activity, and serum cortisol concentration in healthy dogs,” Journal of veterinary internal medicine, vol. 30, iss. 2, pp. 566-573, 2016.

2015

  • K. Höglund, A. Lequarré, I. Ljungvall, K. Mc Entee, A. Merveille, M. Wiberg, V. Gouni, L. J. Willesen, S. Hanås, G. Wess, and others, “Breed Differences In Concentrations Of Neuroendocrines And Cortisol In Healthy Dogs,” Journal of Veterinary Internal Medicine, vol. 29, iss. 1, p. 443, 2015.

  • M. Kierczak, J. Jabłońska, S. K. Forsberg, M. Bianchi, K. Tengvall, M. Pettersson, V. Scholz, J. R. Meadows, P. Jern, Ö. Carlborg, and others, “cgmisc: enhanced genome-wide association analyses and visualization,” Bioinformatics, vol. 31, iss. 23, pp. 3830-3831, 2015.

  • M. Olsson, K. Tengvall, M. Frankowiack, M. Kierczak, K. Bergvall, E. Axelsson, L. Tintle, E. Marti, P. Roosje, T. Leeb, and others, “Genome-wide analyses suggest mechanisms involving early B-cell development in canine IgA deficiency,” PloS one, vol. 10, iss. 7, p. e0133844, 2015.

  • S.K.G. Forsberg, M. Kierczak, I. Ljungvall, A. Merveille, V. Gouni, M. Wiberg, J. L. Willesen, S. Hanås, A. Lequarré, L. M. Sørensen, and others, “The shepherds’ tale: a genome-wide study across 9 dog breeds implicates two loci in the regulation of fructosamine serum concentration in Belgian shepherds,” PloS one, vol. 10, iss. 5, p. e0123173, 2015.

  • K. Tengvall, M. Kierczak, K. Bergvall, M. Olsson, M. Frankowiack, F. H. Farias, G. Pielberg, Ö. Carlborg, T. Leeb, G. Andersson, and others, “Correction: Genome-Wide Analysis in German Shepherd Dogs Reveals Association of a Locus on CFA 27 with Atopic Dermatitis,” PLoS genetics, vol. 11, iss. 12, p. e1005740, 2015.

  • K. Tengvall, S. Kozyrev, M. Kierczak, K. Bergvall, F. Farias, B. Ardesjö-Lundgren, E. Murén, R. Hagman, T. Leeb, G. Pielberg, and others, “A risk haplotype within the PKP2 locus shows association to Canine Atopic Dermatitis and contains cell-type specific enhancers,” , 2015.

  • L. Andersson, M. Kierczak, V. Scholz, J. Johansson, and Å. Hedhammar, “Exploring weight data on over 100,000 Swedish dogs of various breeds,” Acta Veterinaria Scandinavica, vol. 57, iss. 1, p. O8, 2015.

  • M. Bianchi, S. Dahlgren, J. Massey, E. Dietschi, M. Kierczak, M. Lund-Ziener, K. Sundberg, S. I. Thoresen, O. Kämpe, G. Andersson, and others, “A multi-breed genome-wide association analysis for canine hypothyroidism identifies a shared major risk locus on CFA12,” PloS one, vol. 10, iss. 8, p. e0134720, 2015.

2014

  • I. Saha, T. Klingström, S. Forsberg, J. Wikander, J. Zubek, M. Kierczak, and D. Plewczynski, “Evaluation of Machine Learning Algorithms on Protein-Protein Interactions,” in Man-Machine Interactions 3, Springer, 2014, pp. 211-218.

  • K. Sjöstrand, G. Wess, I. Ljungvall, J. Häggström, A-C. Merveille, M. Wiberg, V. Gouni, J. Lundgren Willesen, S. Hanås, A-S. Lequarré, and others, “Breed differences in natriuretic peptides in healthy dogs,” Journal of veterinary internal medicine, vol. 28, iss. 2, pp. 451-457, 2014.

  • I. Saha, J. Zubek, T. Klingström, S. Forsberg, J. Wikander, M. Kierczak, U. Maulik, and D. Plewczynski, “Ensemble learning prediction of protein–protein interactions using proteins functional annotations,” Molecular BioSystems, vol. 10, iss. 4, pp. 820-830, 2014.

  • G. Andersson, K. Bergvall, A. Hedhammer, M. Kierczak, K. Tengvall, and K. Lindblad-Toh, Methods for assessing the risk of canine atopic dermatitisGoogle Patents, 2014.

2013

  • X. Li, M. Kierczak, X. Shen, M. Ahsan, Ö. Carlborg, and S. Marklund, “PASE: a novel method for functional prediction of amino acid substitutions based on physicochemical properties,” Frontiers in genetics, vol. 4, 2013.

  • S. Forsberg, M. Kierczak, A. Merveille, V. Gouni, I. Ljungvall, M. Wiberg, J. Willesen, S. Hanås, A. Lequarré, L. Sørensen, and others, “The Belgian Shepherd’s tale: genome-wide study across 9 dog breeds reveals an association of fructosamine concentration to a locus in Belgian Shepherds,” in The 7th International Conference on Advances in Canine and Feline Genomics and inherited Diseases, 2013.

  • K. Tengvall, M. Kierczak, K. Bergvall, M. Olsson, M. Frankowiack, F. H. Farias, G. Pielberg, Ö. Carlborg, T. Leeb, G. Andersson, and others, “Genome-wide analysis in German shepherd dogs reveals association of a locus on CFA 27 with atopic dermatitis,” PLoS genetics, vol. 9, iss. 5, p. e1003475, 2013.

  • R. M. Nelson, M. Kierczak, and Ö. Carlborg, “Higher order interactions: detection of epistasis using machine learning and evolutionary computation,” Genome-Wide Association Studies and Genomic Prediction, pp. 499-518, 2013.

  • M. Ahsan, X. Li, A. E. Lundberg, M. Kierczak, P. B. Siegel, Ö. Carlborg, and S. Marklund, “Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data,” Frontiers in genetics, vol. 4, 2013.

  • R. M. Nelson, C. Nettelblad, M. E. Pettersson, X. Shen, L. Crooks, F. Besnier, J. M. Álvarez-Castro, L. Rönnegård, W. Ek, Z. Sheng, and others, “MAPfastR: quantitative trait loci mapping in outbred line crosses,” G3: Genes, Genomes, Genetics, vol. 3, iss. 12, pp. 2147-2149, 2013.

  • K. Tengvall, M. Kierczak, K. Bergvall, M. Olsson, M. Frankowiack, F. H. Farias, G. Pielberg, Ö. Carlborg, T. Leeb, G. Andersson, and others, “Correction: Genome-Wide Analysis in German Shepherd Dogs Reveals Association of a Locus on CFA 27 with Atopic Dermatitis,” PLoS genetics, vol. 9, iss. 5, 2013.

  • R.M. Nelson, X. Shen, M. Kierczak, M. Pettersson, Z. Sheng, and O. Carlborg, “MAPfastR Package Tutorial,” , 2013.

  • M. Olsson, L. Tintle, M. Kierczak, M. Perloski, N. Tonomura, A. Lundquist, E. Murén, M. Fels, K. Tengvall, G. Pielberg, and others, “Thorough investigation of a canine autoinflammatory disease (AID) confirms one main risk locus and suggests a modifier locus for amyloidosis,” PloS one, vol. 8, iss. 10, p. e75242, 2013.

  • A. Richter-Boix, M. Quintela, M. Kierczak, M. Franch, and A. Laurila, “Fine-grained adaptive divergence in an amphibian: genetic basis of phenotypic divergence and the role of nonrandom gene flow in restricting effective migration among wetlands,” Molecular ecology, vol. 22, iss. 5, pp. 1322-1340, 2013.

2012

  • M. Owczarek-Lipska, B. Lauber, V. Molitor, S. Meury, M. Kierczak, K. Tengvall, M. T. Webster, V. Jagannathan, Y. Schlotter, T. Willemse, and others, “Two loci on chromosome 5 are associated with serum IgE levels in Labrador retrievers,” PloS one, vol. 7, iss. 6, p. e39176, 2012.

2011

M. Dramiński, M. Kierczak, A. Nowak-Brzezińska, J. Koronecki, and J. Komorowski, “The Monte Carlo feature selection and interdependency discovery is unbiased,” Control and Cybernetics, vol. 40, pp. 199-211, 2011.

2010

  • M. Dramiński, M. Kierczak, J. Koronacki, and J. Komorowski, “Monte Carlo feature selection and interdependency discovery in supervised classification,” Advances in Machine Learning II, pp. 371-385, 2010.

  • M. Kierczak, M. Dramiński, J. Koronacki, and J. Komorowski, “Computational analysis of molecular interaction networks underlying change of HIV-1 resistance to selected reverse transcriptase inhibitors,” Bioinformatics and biology insights, vol. 4, p. 137, 2010.

2009

  • M. Kierczak, M. Dramiński, J. Koronacki, and J. Komorowski, “Analysis of local molecular interaction networks underlying HIV-1 resistance to reverse transcriptase inhibitors.,” , 2009.

  • M. Kierczak, “From Physicochemical Features to Interdependency Networks: A Monte Carlo Approach to Modeling HIV-1 Resistome and Post-translational Modifications,” PhD Thesis, 2009.

  • M. Kierczak, K. Ginalski, M. Dramiński, J. Koronacki, W. Rudnicki, and J. Komorowski, “A rough set-based model of HIV-1 reverse transcriptase resistome,” Bioinformatics and biology insights, vol. 3, p. 109, 2009.

  • M. Kierczak, D. Plewczyński, G. Andersson, L. Samuel, W. Rudnicki, M. Drami’nski, J. Koronacki, K. Ginalski, and J. Komorowski, “A Monte Carlo approach to modeling post-translational modification sites using local physicochemical properties.,” , 2009.

2008

  • M. Kierczak, W. Rudnicki, and J. Komorowski, “Construction of Rough Set-Based Classifiers for Predicting HIV Resistance to Nucleoside Reverse Transcriptase Inhibitors,” Granular Computing: At the Junction of Rough Sets and Fuzzy Sets, pp. 249-258, 2008.

2006

  • W. Rudnicki, M. Kierczak, J. Koronacki, and J. Komorowski, “A statistical method for determining importance of variables in an information system,” in Rough Sets and Current Trends in Computing, 2006, pp. 557-566.

PhD Thesis

Introduction to my PhD thesis: From Physicochemical Properties to Interdependency Networks: A Monte Carlo Approach to Modeling HIV-1 Resistome and Post-translational Modifications. You can download it here. The thesis has been defended on the 15th of Dec 2009 and the opponent was prof. Anna Tramontano from La Sapienza University, Rome. The thesis was supervised by prof. Jan Komorowski from The Linnaeus Centre for Bioinformatics.

Conferences, Workshops, Talks

  • European Human Genetics Conference Amsterdam, The Netherlands, 2011. Dealing with population stratification in GWAS studies. poster. 15th QTLMAS Workshop Rennes, France, 2011. A simulation-based approach to controlling FPR in GWAS. presentation.
  • Bioinformatics 2008 Warsaw, Poland 2008. Kierczak M, Plewczyński D, Rudnicki WR, Komorowski J. Random forest-based predictive models of post-translational modifications in proteins. poster awarded an oral presentation.

Wikipedia Contributions

In chronological order

  • Eduardo Galeano (pl)
  • Gnawa (pl)
  • Teoria zbiorów przybliżonych (pl)
  • Kroswalidacja stratyfikowana (pl, contribution to the main article)
  • Bruno Liljefors (pl)
  • Wiolaceina (pl)
  • Aguaviva (pl)