The present invention relates to a method and a system applying tensor flow-based deep-learning technology disclosed by Google after transforming a nucleotide sequence of a population genome into a two-dimensional image for grasping human genetic characteristics, The present invention relates to biomarker discovery based on disease-related, biomarker and patient layering studies with improved accuracy and efficiency. According to the present invention, when three billion one-dimensional nucleotide sequences are expressed as a two-dimensional image, disease-related, biomarker and patient layering studies including gene-gene interaction and causing multiple genes such as complex diseases (such as diabetes, hypertension, and osteoporosis) can be efficiently detected. In addition, in terms of speed as well, calculation by CNN using deep leaning two-dimensional image can be 54,000 times that of searching three billion one-dimension nucleotide sequences in the case of human genome and the speed improvement and precision improvement can be approximately 1,400 times the two-dimensional when the two-dimensional image is searched three-dimensionally.
Method for deep learning-based biomarker discovery with conversion data of genome sequences