ScholarWorks@숙명여자대학교

초록

Agricultural genetic resources are basic raw materials in biotechnology. To improve productivity and successful crossbreeding combinations, feature analysis utilizing the genetic resources should be performed to identify important attributes. In recent, data analysis based on machine learning (ML) algorithms has been proved to show high performance in various fields, which shows that it can also be utilized for agricultural genetic resource datasets. However, as it requires understanding and implementation for each ML technique, ML-based research for genetic resources is limited. In this paper, we present a machine learning-based agricultural genetic resource analysis platform for easy access. After preprocessing the dataset from users, ML-based methods including clustering, classifi-cation, feature selection, and correlation analysis were performed, and each result was visualized. We believe that our platform can facilitate data analysis by utilizing agricultural genetic resources.

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