Building a Useful Article Recommendation System for BioDOS

The authors aim to develop an integrated interface, Bio Discovery OS (BioDOS), that can present DNA sequences encoding various biomolecular networks corresponding to a desired behavior. Its development is being conducted as part of a team-based research project (Core Research for Evolutional Science and Technology; CREST). This CREST is broadly divided into the design of sequences by hybrid AI to generate a large number of candidate biomolecular networks followed by their refinement (Dry study), and the construction of a system based on isomorphic combinations guaranteed by mathematical models and biological experiments to confirm its operation (Wet study). Especially, the authors focus on determining the similarity with existing networks in the Dry study. It is required to narrow down the large number of candidate networks generated by machine learning and reinforcement learning by matching the successes of existing articles, biological experiments, and numerical simulations. In this paper, the authors aim to propose a method for discovering useful articles for BioDOS using various machine learning methods such as deep learning, and construct an article recommendation system for BioDOS using the proposed method. Numerical experiments are conducted to verify the effectiveness of the proposed method and the constructed recommendation system.