Hiki, Yusuke

写真a

Affiliation

Faculty of Science and Technology, Department of Biosciences and Informatics ( Yagami )

Position

Assistant Professor (Non-tenured)/Research Associate (Non-tenured)/Instructor (Non-tenured)
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Papers 【 Display / hide 】

  • Mixing features of transcription factors and genes enable accurate prediction of gene regulation relationships for unknown transcription factors

    Okubo R., Morikura T., Hiki Y., Tokuoka Y., Kobayashi T.J., Yamada T.G., Funahashi A.

    Nar Genomics and Bioinformatics 8 ( 1 )  2026.03

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    Identifying regulatory relationships between transcription factors (TFs) and genes is essential to understand diverse biological phenomena related to gene expression. Recently, deep learning–based models to predict TFs that bind to genes from nucleotide sequences of the target genes have been developed, yet these models are trained to predict known TFs only. Here, we developed a deep learning model, GReNIMJA (Gene Regulatory Network Inference by Mixing and Jointing features of Amino acid and nucleotide sequences), to predict gene regulation even by unknown TFs. Our model is designed to mix the features of the TF amino acid sequences and nucleotide sequences of the target genes using a 2D Long Short-Term Memory architecture and to perform binary classification with the aim of determining the presence or absence of a regulatory relationship. By explicitly modeling interactions between TFs and genes, our model can predict gene regulation for unknown TFs. The accuracy of our model in predicting regulatory relationships was 84.4% for known TFs (higher than those of conventional models) and 68.5% for unknown TFs; the latter is an unsolved task for conventional deep learning-based models. We expect our model to advance identification of unknown gene regulatory networks and contribute to the understanding of diverse biological phenomena.

  • Asynchronous batch Bayesian optimization with pipelining evaluations in experimental equipment-limited situations

    Taguchi Y., Shibuya Y., Hiki Y., Morikura T., Yamada T.G., Funahashi A.

    Slas Technology 37 2026.03

    ISSN  24726303

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    Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data, and then the model suggests parameters that would most likely improve the results. Batch Bayesian optimization reduces the processing time of optimization by parallelizing experiments. However, batch Bayesian optimization cannot be applied if the number of parallelized experiments is limited by the cost or scarcity of equipment; in such cases, sequential methods require an unrealistic amount of time. In this study, we developed pipelining Bayesian optimization (PipeBO) to reduce the processing time of optimization even with a limited number of parallel experiments. PipeBO was inspired by the pipelining of central processing unit architecture, which divides computational tasks into multiple processes. PipeBO was designed to achieve experiment parallelization by overlapping various processes of the experiments. PipeBO uses the results of completed experiments to update the parameters of running parallelized experiments. PipeBO was mathematically formulated by modeling experiments as multiple processes with asynchronous result arrival, enabling partial model updates in a pipelined fashion. Using the Black-Box Optimization Benchmarking, which consists of 24 benchmark functions, we compared PipeBO with the sequential Bayesian optimization methods. PipeBO reduced the average processing time of optimization to about 56% for the experiments that consisted of two processes or even less for those with more processes for 20 out of the 24 functions. Overall, PipeBO parallelizes Bayesian optimization in experimental equipment–limited situations so that efficient optimization can be achieved.

  • Stepwise changes in gene expression inducing anhydrobiotic state transition and the gene regulatory network in Polypedilum vanderplanki larvae

    Hiki Y., Yamada T.G., Cornette R., Gusev O., Shagimardanova E., Kikawada T., Funahashi A.

    Biochemical and Biophysical Research Communications 800 2026.02

    ISSN  0006291X

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    The larva of the sleeping chironomid, Polypedilum vanderplanki, is the only insect capable of extreme desiccation tolerance, known as anhydrobiosis. The larvae can survive near-complete desiccation and, upon rehydration, rapidly resume metabolism and return to their normal life cycle. Activation of genes involved in antioxidant activity, protection of biomolecules, and DNA repair is required for desiccation tolerance. In the desiccation-tolerant P. vanderplanki cell line Pv11, the key factors of the regulatory network for these genes are heat shock factor (Hsf) and nuclear transcription factor Y subunit C (NF-YC). However, how desiccation tolerance is established at the whole-body level remains unknown. To unravel the dynamic response to desiccation at this level, it is necessary to clarify which molecules need to function for desiccation tolerance and when. Here, we newly acquired and analyzed detailed time-series gene expression data to reveal the regulatory mechanisms underlying the transition to an ametabolic state during desiccation. We showed that the acquisition of desiccation tolerance requires the expression of a number of desiccation-inducible genes in a specific order. This order can be explained by a gene regulatory network triggered by nuclear transcription factor Y subunit A (NF-YA) and Sine oculis-related homeobox 4 (Six4). To our knowledge, this is the first report that desiccation tolerance is related to stepwise changes in gene expression in response to desiccation.

  • Inference of Gene Regulatory Networks for Overcoming Low Performance in Real-World Data

    Hiki Y., Tokuoka Y., Yamada T.G., Funahashi A.

    IEEE Access 14   18013 - 18024 2026

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    The identification of gene regulatory networks is important for understanding the mechanisms of various biological phenomena. Many methods have been proposed to infer networks from time-series gene expression data obtained by high-throughput next-generation sequencings. Such methods can effectively infer gene regulatory networks for in silico data, but inferring the networks accurately from in vivo data remains a challenge because of the large noise and low time sampling rate. Here, we proposed a novel unsupervised learning method, Multi-view attention Long Short-term memory for Network inference (MaLoN). It can infer gene regulatory networks with temporal changes in gene regulation using the multi-view attention Long Short-term memory model. MaLoN inferred gene regulatory networks more accurately than 6 existing methods for in vivo benchmark Saccharomyces cerevisiae and Escherichia coli datasets with small numbers of genes, but less accurate for large-scale datasets. Therefore, MaLoN is currently effective for small-scale datasets only. The ablated models indicated that the multi-view attention mechanism suppressed false positives. The order of activation of gene regulations inferred by MaLoN was consistent with existing knowledge.

  • A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics

    Zhang M., Hiki Y., Funahashi A., Kobayashi T.J.

    Npj Systems Biology and Applications 10 ( 1 )  2024.12

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    Predicting olfactory perceptions from odorant molecules is challenging due to the complex and potentially discontinuous nature of the perceptual space for smells. In this study, we introduce a deep learning model, Mol-PECO (Molecular Representation by Positional Encoding of Coulomb Matrix), designed to predict olfactory perceptions based on molecular structures and electrostatics. Mol-PECO learns the efficient embedding of molecules by utilizing the Coulomb matrix, which encodes atomic coordinates and charges, as an alternative of the adjacency matrix and its Laplacian eigenfunctions as positional encoding of atoms. With a comprehensive dataset of odor molecules and descriptors, Mol-PECO outperforms traditional machine learning methods using molecular fingerprints and graph neural networks based on adjacency matrices. The learned embeddings by Mol-PECO effectively capture the odor space, enabling global clustering of descriptors and local retrieval of similar odorants. This work contributes to a deeper understanding of the olfactory sense and its mechanisms.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Reviews, Commentaries, etc. 【 Display / hide 】

  • Corrigendum to: COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms (Molecular Systems Biology, (2021), 17, 10, 10.15252/msb.202110387)

    Ostaszewski M., Niarakis A., Mazein A., Kuperstein I., Phair R., Orta-Resendiz A., Singh V., Aghamiri S.S., Acencio M.L., Glaab E., Ruepp A., Fobo G., Montrone C., Brauner B., Frishman G., Monraz Gómez L.C., Somers J., Hoch M., Kumar Gupta S., Scheel J., Borlinghaus H., Czauderna T., Schreiber F., Montagud A., Ponce de Leon M., Funahashi A., Hiki Y., Hiroi N., Yamada T.G., Dräger A., Renz A., Naveez M., Bocskei Z., Messina F., Börnigen D., Fergusson L., Conti M., Rameil M., Nakonecnij V., Vanhoefer J., Schmiester L., Wang M., Ackerman E.E., Shoemaker J.E., Zucker J., Oxford K., Teuton J., Kocakaya E., Summak G.Y., Hanspers K., Kutmon M., Coort S., Eijssen L., Ehrhart F., Rex D.A.B., Slenter D., Martens M., Pham N., Haw R., Jassal B., Matthews L., Orlic-Milacic M., Senff-Ribeiro A., Rothfels K., Shamovsky V., Stephan R., Sevilla C., Varusai T., Ravel J.M., Fraser R., Ortseifen V., Marchesi S., Gawron P., Smula E., Heirendt L., Satagopam V., Wu G., Riutta A., Golebiewski M., Owen S., Goble C., Hu X., Overall R.W., Maier D., Bauch A., Gyori B.M., Bachman J.A., Vega C., Grouès V., Vazquez M., Porras P., Licata L., Iannuccelli M., Sacco F., Nesterova A., Yuryev A., de Waard A., Turei D., Luna A., Babur O.

    Molecular Systems Biology 17 ( 12 )  2021.12

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Courses Taught 【 Display / hide 】

  • BIOPROGRAMMING 1

    2026

  • BIOPROGRAMMING 2

    2026

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