Current address:

Institute of Informatics, University of Warsaw
Banacha 2
Warsaw, Mazovian
Poland

Education:

Ph.D. in Biochemistry (Bioinformatics)
Institute of Biochemistry and Biophysics of the Polish Academy of Science
2008 - 2013

BSc in Computer Science
Jan Kochanowski University of Humanities and Sciences
2005 - 2007

MSc in Biology (Genetics)
Jan Kochanowski University of Humanities and Sciences
2004 - 2006

Experience:

Assistant Professor Institute of Informatics, University of Warsaw 2018 -

CEO & Owner Solid Scientometrics 2018 -

Postdoctoral Researcher Max Planck Institute for Biophysical Chemistry, Goettingen, Germany 2015 - 2017

Skills:

machine learning: deep learning, ANN, SVM, genetic algorithms, optimisation

programming languages: PYTHON, R, C++, C, Java, shell

databases: MySQL, PostgreSQL

Internet: HTML, PHP, Zope, Django, Apache, JavaScript

in-depth knowledge of bioinformatics programs (e.g. BLAST, HHSearch, ClustalW, MAFFT, UCSD Chimera, VMD, Modeller, MEME, and few hundred others)

Activities:

International Society for Computational Biology (ISCB)

Polish Bioinformatics Society (PTBI)

Polish Biochemical Society (PTBioch)

Society for Bioinformatics in Northern Europe (SocBiN)

Interests:

development of bioinformatics tools for protein and RNA

next-generation sequencing (NGS) data analysis

proteomics

protein structure modeling

intrinsic protein disorder and its function in biological systems

References:

Kozlowski LP, Bujnicki JM MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics 2012; 13:111. doi: 10.1186/1471-2105-13-11

Kozlowski LP IPC - Isoelectric Point Calculator. Biology Direct 2016; 11:55. doi: 10.1186/s13062-016-0159-9

Kozlowski LP Proteome-pI: Proteome Isoelectric Point Database. Nucleic Acids Res. 2017; 45 (D1): D1112-D1116

Kozlowski LP IPC 2.0 -- prediction of isoelectric point and pKa dissociation constants. Nucleic Acids Res. 2021; doi: 10.1093/nar/gkab295

Erijman A, Kozlowski L, et al. A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning. Molecular Cell; 78, 1--13. doi: 10.1016/j.molcel.2020.04.020

Other:

Personal website: http://bioinformatics.netmark.pl/

URL to updated CV: http://goo.gl/JgTHzC