慶應義塾研究者情報データベース

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• 複雑な生命現象の仕組みを理解するためには個々の遺伝子情報のみでは不十分であり、それらの相互作用を明らかにする必要があります。そこで、生物が持つネットワークをデータから推定する基盤技術の開発やネットワークを用いた解析手法の構築、それらを用いたデータ解析によって、生物が多様な能力を持つことができる仕組みの解明を目指します。

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• 2021年04月

  -

  2024年03月

  日本学術振興会特別研究員(DC1)

• 2024年04月

  -

  2026年03月

  慶應義塾大学研究員(有期)(非常勤)(大学院理工学研究科)

学歴 【 表示 ／ 非表示 】

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• 2015年04月

  -

  2019年03月

  慶應義塾大学, 理工学部, 生命情報学科

  大学, 卒業

• 2019年04月

  -

  2021年03月

  慶應義塾大学, 理工学研究科, 基礎理工学専攻

  大学院, 修了, 修士

• 2021年04月

  -

  2026年03月

  慶應義塾大学, 理工学研究科, 基礎理工学専攻

  大学院, 修了, 博士

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学位 【 表示 ／ 非表示 】

• 博士(理学), 慶應義塾大学, 課程, 2026年03月

  ネムリユスリカ幼虫の乾燥刺激に応答する遺伝子制御ネットワークの解析および基盤技術の開発

研究分野 【 表示 ／ 非表示 】

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• ライフサイエンス / システムゲノム科学

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• システム生物学

• ネットワーク生物学

• バイオインフォマティクス

• 機械学習

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• 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年02月

  　概要を見る

  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.

  • Access to Document (DOI)

• 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年01月

  　概要を見る

  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.

  • Access to Document (DOI)

• 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年01月

  ISSN  24726303

  　概要を見る

  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.

  • Access to Document (DOI)

• 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年01月

  ISSN  0006291X

  　概要を見る

  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.

  • Access to Document (DOI)

• 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年07月

  　概要を見る

  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.

  • Access to Document (DOI)

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KOARA（リポジトリ）収録論文等 【 表示 ／ 非表示 】

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• 遺伝子数増大に耐える高精度遺伝子制御ネットワーク推定法の提案

  比企, 佑介

  科学研究費補助金研究成果報告書 2020年

総説・解説等 【 表示 ／ 非表示 】

総説・解説等 【 表示 ／ 非表示 】

• ネムリユスリカの乾燥耐性をシステムとして理解する

  比企佑介,山田貴大,舟橋啓

  細胞 55 （ 14 ） 43 - 47 2023年12月

  記事・総説・解説・論説等（学術雑誌）, 筆頭著者

研究発表 【 表示 ／ 非表示 】

研究発表 【 表示 ／ 非表示 】

• ネムリユスリカ幼虫の乾燥刺激に応答する遺伝子制御ネットワークの解析

  比企佑介

  [国内会議]  第10回クマムシ学研究会 （横浜, 日本） , 

  2026年03月

  , 

  口頭発表（一般）

• Inference of dynamic gene regulatory network overcoming low performance for real world data using multi-view attention LSTM

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

  [国際会議]  International Conference on Systems Biology 2023 （Hartford, USA） , 

  2023年10月

  , 

  口頭発表（一般）

• ネムリユスリカ乾燥耐性獲得時における無代謝状態遷移機構に関する研究

  比企佑介

  [国内会議]  第一回乾眠生物若手の会, 

  2021年12月

  , 

  口頭発表（一般）

• Inference of transcriptional regulatory network driven by desiccation and rehydration in Polypedilum vanderplanki

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

  [国際会議]  Moscow Conference on Computational Molecular Biology 2019 （Moscow, Russia） , 

  2018年09月

  , 

  口頭発表（招待・特別）

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受賞 【 表示 ／ 非表示 】

• AIPネットワークラボ長賞

  比企佑介, 2022年03月, JST AIPチャレンジプログラム, 深層学習を用いた遺伝子制御ネットワーク推定手法の開発

  受賞区分： 国内外の国際的学術賞

担当授業科目 【 表示 ／ 非表示 】

担当授業科目 【 表示 ／ 非表示 】

• バイオプログラミング第１

  2026年度

• バイオプログラミング第２

  2026年度

• 基礎生命実験

  2026年度, 秋学期, 学部専門科目, 実習・実験

• 生命情報実験B

  2026年度, 春学期, 学部専門科目, 実習・実験

• 生命情報実験A

  2026年度, 春学期, 学部専門科目, 実習・実験

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