2018 - Fellow of the Indian National Academy of Engineering (INAE)
2012 - ACM Senior Member
The scientist’s investigation covers issues in Artificial intelligence, Data mining, Pattern recognition, Computational biology and Protein secondary structure. Artificial intelligence connects with themes related to Machine learning in his study. His Data mining research integrates issues from Missing data, Imputation, Support vector machine and Process.
His biological study spans a wide range of topics, including Bioinformatics and Protein structural class. His Computational biology research incorporates elements of Amino acid, Proteome, Molecular recognition, Intrinsically disordered proteins and In silico. The various areas that he examines in his Protein secondary structure study include Protein structure and Sequence alignment.
Lukasz Kurgan spends much of his time researching Artificial intelligence, Computational biology, Machine learning, Protein secondary structure and Intrinsically disordered proteins. Artificial intelligence is closely attributed to Pattern recognition in his work. His Computational biology study also includes fields such as
Lukasz Kurgan works mostly in the field of Protein secondary structure, limiting it down to topics relating to Protein structure and, in certain cases, Crystallography, Algorithm and Protein crystallization, as a part of the same area of interest. Lukasz Kurgan interconnects Proteome, Genetics, Cell biology, Protein folding and Translation in the investigation of issues within Intrinsically disordered proteins. His work on Knowledge extraction as part of general Data mining study is frequently linked to Web server, therefore connecting diverse disciplines of science.
His primary areas of study are Computational biology, Intrinsically disordered proteins, Protein sequencing, Artificial intelligence and Human proteome project. His Computational biology research includes themes of Plasma protein binding, Protein–protein interaction, RNA, Conserved sequence and Sequence. Lukasz Kurgan has included themes like Nucleic acid, Selection, Benchmark, Translation and Protein level in his Intrinsically disordered proteins study.
His Protein sequencing research incorporates themes from Annotation and Molecular recognition. His studies in Artificial intelligence integrate themes in fields like Machine learning, ENCODE and Pattern recognition. His Human proteome project research is multidisciplinary, incorporating elements of Druggability, Proteome, Drug discovery and Drug repositioning.
Computational biology, Human proteome project, Protein sequencing, Proteome and Conserved sequence are his primary areas of study. His Computational biology study integrates concerns from other disciplines, such as Plasma protein binding, RNA, Drug protein interactions, Drug repositioning and Protein level. The study incorporates disciplines such as Amino acid and DNA in addition to RNA.
The Protein sequencing study which covers Sequence that intersects with Sequence alignment, Protein moonlighting and Random forest. His Proteome study combines topics in areas such as Protein structure, Statistics and Three-domain system. His Artificial neural network study necessitates a more in-depth grasp of Artificial intelligence.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
Daniel J. Klionsky;Amal Kamal Abdel-Aziz;Sara Abdelfatah;Mahmoud Abdellatif.
Autophagy (2021)
Data Mining: A Knowledge Discovery Approach
Krzysztof J. Cios;Witold Pedrycz;Roman W. Swiniarski;Lukasz Andrzej Kurgan.
(2007)
CAIM discretization algorithm
L.A. Kurgan;K.J. Cios.
IEEE Transactions on Knowledge and Data Engineering (2004)
Genetic learning of fuzzy cognitive maps
Wojciech Stach;Lukasz Kurgan;Witold Pedrycz;Marek Reformat.
Fuzzy Sets and Systems (2005)
A survey of Knowledge Discovery and Data Mining process models
Lukasz A. Kurgan;Petr Musilek.
Knowledge Engineering Review (2006)
D2P2: database of disordered protein predictions
Matt E. Oates;Pedro Romero;Takashi Ishida;Mohamed F. Ghalwash.
Nucleic Acids Research (2012)
Impact of imputation of missing values on classification error for discrete data
Alireza Farhangfar;Lukasz Kurgan;Jennifer Dy.
Pattern Recognition (2008)
MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins
Fatemeh Miri Disfani;Wei-Lun Hsu;Marcin J. Mizianty;Christopher J. Oldfield.
Bioinformatics (2012)
Knowledge discovery approach to automated cardiac SPECT diagnosis
Lukasz A. Kurgan;Krzysztof J. Cios;Ryszard Tadeusiewicz;Marek Ogiela.
Artificial Intelligence in Medicine (2001)
SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles
Eshel Faraggi;Tuo Zhang;Tuo Zhang;Yuedong Yang;Yuedong Yang;Lukasz A. Kurgan;Lukasz A. Kurgan.
Journal of Computational Chemistry (2012)
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