| Budget Amount *help |
¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2020: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Outline of Final Research Achievements |
Position-specific substitution matrices (PSSMs) are matrices, which include evolutionary information about amino acids. PSSMs are fundamental information for sequence similarity search, evolutionary analysis of amino acids, etc. However, in order to generate PSSMs, it is necessary to perform repeated sequence similarity searches on a large database, which takes a lot of time. In the study, we have developed an artificial intelligence (AI), SPBuild, which could reduce the generation time of PSSMs, keeping information contents of the generated PSSMs. To develop SPbuild, we had utilized a recurrent neural network (RNN). Through the research, we realized that development of AI with existing RNNs would take a lot of time, due to its large time complexity. Thus, we had developed a novel RNN, YamRNN, which showed better convergence performance compared to existing RNNs. SPBuild and YamRNN is publicly available.
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