Leonidas P. Karakatsanis

Leonidas P. Karakatsanis
Democritus University of Thrace | DUTH · Department of Environmental Engineering

Phd
Currently, we study the information of the human genome by applying tools from complexity theory and ML approaches.

About

40
Publications
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Introduction
Currently, we study the information of the human genome by applying tools from complexity theory and ML approaches.
Additional affiliations
December 2018 - present
Democritus University of Thrace
Position
  • Laboratory Teaching Staff Member
Description
  • Research Team of Chaos and Complexity.
Education
January 2010 - April 2013
Democritus University of Thrace
Field of study
  • Complexity - Tsallis Statistics - SOC - Non linear dynamics
September 1985 - September 1991
Democritus University of Thrace
Field of study
  • Electrical Engineer

Questions

Question (1)
Question
I am looking for a series of datasets (raw-data) in brain (EEG or MEG) or cardiac (HRV) activity of patients under homeopathic treatment. We want to research the phase transition of the human holistic complex system with modern tools of nonlinear analysis and complexity theory.
Thank you for your attention.

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Cited By

Projects

Projects (4)
Project
This projects aims to look into the temporal evolution of scaling but also any other metric that depends of financial time-series (macro-economical, stock market etc,). The purpose is to detect relevant temporal changes in the metric that can be used, for example, as warning/signaling tool for future events.
Project
The identification of the symmetries and fundamental laws which produces the order of information in all genome and generate strange dynamics which is observable and qualitatively measurable based on complexity metrics and Machine Learning models.
Project
This project combines two independent domains of science, the high throughput DNA sequencing capabilities of Genomics and complexity theory from Physics, to assess the information encoded by the different genomic segments of exonic, intronic and intergenic regions of the Major Histocompatibility Complex (MHC) and identify possible interactive relationships and motifs of information.