What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Machine learning
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.
His most cited work include:
- Data Mining: A Knowledge Discovery Approach (414 citations)
- Genetic learning of fuzzy cognitive maps (373 citations)
- D2P2: database of disordered protein predictions (359 citations)
What are the main themes of his work throughout his whole career to date?
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
- Protein sequencing, which have a strong connection to Structural genomics and Sequence alignment,
- RNA which connect with Plasma protein binding.
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.
He most often published in these fields:
- Artificial intelligence (33.59%)
- Computational biology (31.30%)
- Machine learning (18.70%)
What were the highlights of his more recent work (between 2017-2021)?
- Computational biology (31.30%)
- Intrinsically disordered proteins (18.70%)
- Protein sequencing (11.83%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition) (38 citations)
- Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains. (37 citations)
- DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites (35 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Artificial intelligence
- Machine learning
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.
