Dr. Attila Lehotzky – MTA TTK, Institute of Enzymology,Hungary
Title: Cuteness, smartness and other levels in biological systems (Neuronal networks, machine deep-learning)
Abstract: Artificial intelligence originally is defined as self-repairing and self-orginizing ability of machines. Nowadays, the concept is used much broader meaning. In genome studies, enormous data load (4 billion basepair in a human genome, personalized about a few hundred thousand Single Nucleotide Polymorpisms (SNPs) is well over in numbers and connections what a human brain can handle. Specified computers and sofware languages help to compare genomic data from different levels to generate the possibility of personalized medicines. For these aims, machine deep learning and neurologistic learning as computer techniques is applyied to compare genomic data sets and create desriptors of a given medical condition, i.e. like SNP sets. Albeight the Holy Gral is not reached yet, the concept of genotype-phenotype connection and personalised disease evaluation (probability of a given disease for a person) is on the close horizont as the methods, services, and techniques are developing and creating smart systems for genome sciences. Moreover, transient effects from environment on genome and disease state could be evaluated by these newly developed smart systems targeting epigenomic changes. The generated results already increase the development in science and human healthcare.