Biology subjects

Yamada, K. D.

Publications and source records attributed to Yamada, K. D..

3 recordsLinked to original sources

bioRxiv2018

Hyperparameter-free optimizer of gradient-descent method incorporating unit correction and moment estimation

In the deep learning era, stochastic gradient descent is the most common method used for optimizing neural network parameters. Among the various mathematical optimization methods, the gradient descent method is the most naive. Adjustment of learning rate is necessary for quick convergence, which is normally done manually with gradient descent. Many optimizers have been developed to control the learning rate and increase convergence speed. Generally, these optimizers adjust the learning rate automatically in response to learning status. These optimizers were gradually improved by incorporating the effective aspects of earlier methods. In this study, we developed a new optimizer: YamAdam. Our optimizer is based on Adam, which utilizes the first and second moments of previous gradients. In addition to the moment estimation system, we incorporated an advantageous part of AdaDelta, namely a unit correction system, into YamAdam. According to benchmark tests on some common datasets, our optimizer showed similar or faster convergent performance compared to the existing methods. YamAdam is an option as an alternative optimizer for deep learning.

bioinformatics↗

bioRxiv2017

De novo profile generation based on sequence context specificity with the long short-term memory network

Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profiles are widely used for bioinformatics studies, such as sequence similarity searches, multiple alignments, and evolutionary analyses. Currently, many biological sequences are becoming available, and the rapidly increasing amount of sequence data emphasizes the importance of scalable generators of amino acid sequence profiles. We employed the LSTM network and developed a novel profile generator to construct profiles without any assumptions, except for input sequence context. Our method could generate better profiles than existing de novo profile generators, including CSBuild and RPS-BLAST, on the basis of profile-sequence similarity search performance with linear calculation costs against input sequence size. In addition, we analyzed the effects of the memory power of LSTM and found that LSTM had high potential power to detect long-range interactions between amino acids, as in the case of beta-strand formation, which has been a difficult problem in protein bioinformatics using sequence information. We demonstrated the importance of sequence context and the feasibility of LSTM on biological sequence analyses. Our results demonstrated the effectiveness of memories in LSTM and showed that our de novo profile generator, SPBuild, achieved higher performance than that of existing methods for profile prediction of beta-strands, where long-range interactions of amino acids are important and are known to be difficult for the existing window-based prediction methods. Our findings will be useful for the development of other prediction methods related to biological sequences by machine learning methods.

bioinformatics↗

bioRxiv2017

Optimizing scoring function of dynamic programming of pairwise profile alignment using derivative free neural network

A profile comparison method with position-specific scoring matrix (PSSM) is one of the most accurate alignment methods. Currently, cosine similarity and correlation coefficient are used as scoring functions of dynamic programming to calculate similarity between PSSMs. However, it is unclear that these functions are optimal for profile alignment methods. At least, by definition, these functions cannot capture non-linear relationships between profiles. Therefore, in this study, we attempted to discover a novel scoring function, which was more suitable for the profile comparison method than the existing ones. Firstly we implemented a new derivative free neural network by combining the conventional neural network with evolutionary strategy optimization method. Next, using the framework, the scoring function was optimized for aligning remote sequence pairs. Nepal, the pairwise profile aligner with the novel scoring function significantly improved both alignment sensitivity and precision, compared to aligners with the existing functions. Nepal improved alignment quality because of adaptation to remote sequence alignment and increasing the expressive power of similarity score. The novel scoring function can be realized using a simple matrix operation and easily incorporated into other aligners. With our scoring function, the performance of homology detection and/or multiple sequence alignment for remote homologous sequences would be further improved.

bioinformatics↗