신경망은 심전도 신호, 망막 영상, 지진파 등 잡음이 포함된 자료의 전처리 작업에 활용되고 있다. 그러나, 잡음의

전처리는 전산시간 증가, 원본 신호의 왜곡등의 문제점을 내포하고 있다. 본 연구에서는 잡음의 전처리 없이 측정 자

료를 분석할 수 있는 신경망 구조를 연구하였다. 신경망의 학습 자료로써 잡음이 포함된 DNA 용액의 동역학적 거동을

선정하여, 해당 자료로부터 DNA 용액의 조성비를 예측하고자 하였다. DNA의 동역학 자료에 인위적으로 백색 잡음을

추가하여, 신경망의 예측에 대한 잡음의 영향을 알아보았다. 결과적으로, 잡음의 전처리 없이 O(1)의 신호 대 잡음비

자료로부터 O(0.01)의 오차로 용액의 조성비를 예측할 수 있었다. 이러한 연구 결과는 측정 잡음에 민감하게 영향 받을

수 있는 극미량의 유전병 또는 암세포와 관련된 DNA를 분석을 위한 핵심 인공지능 기술로 활용할 수 있다.

A neural network is utilized for preprocessing of de-noizing in electrocardiogram signals, retinal images, seismic waves, etc. However, the de-noizing process could provoke increase of computational time and distortion of the original signals. In this study, we investigated a neural network architecture to analyze measurement data without additional de-noizing process. From the dynamical behaviors of DNA in aqueous solution, our neural network model aimed to predict the mole fraction of each DNA in the solution. By adding white noise to the dynamics data of DNA artificially, we investigated the effect of the noise to neural network’s predictions. As a result, our model was able to predict the DNA mole fraction with an error of O(0.01) when signal-to-noise ratio was O(1). This work can be applied as a efficient artificial intelligence methodology for analyzing DNA related to genetic disease or cancer cells which would be sensitive to background measuring noise.

Neural network, Prediction mole fraction of DNA, White noise, Signal-to-noise-ratio

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