コンテンツにジャンプ

トップページ > 研究組織一覧 > 分野・独立ユニットグループ > 独立ユニットグループ > 計算生命科学ユニット > 論文業績

論文業績

Researchmap ／ Google Scholar

2026

原著論文

  1. Nomura S, Kojima Y (Co-Correspo), Minoura K, Hayashi S, Abe K, Hirose H, Shimamura T. mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities. Bioinformatics. 2026;42(9):btag652. DOI: 10.1093/bioinformatics/btag652
  2. Umemura K, Kojima Y, Julamanee J, Okuno Y, Takeuchi Y, Ohara F, Kuwano S, Adachi Y, Hanajiri R, Terakura S, Kiyoi H. BATF3 regulates differentiation of CD8+ T lymphocytes and memory differentiation program. Life Science Alliance. 2026;9(7):e202503550. DOI: 10.26508/lsa.202503550
  3. Okuhiro Y, Ito S, Watanabe K, Yan Y, Kumagai K, Sato T, Kojima Y, Fujioka Y, Takahashi N, Kiyoi H, Maeda Y, Kato T, Nishikawa H. Ponatinib inhibits LCK and PI3K signaling and promotes CD8+ T stem cell memory cell development. Nature Communications. 2026;17:4934. DOI: 10.1038/s41467-026-71375-2
  4. Tsuji T, Hirose H, Sugiyama D, Shindo M, Hartantyo RY, Saito Y, Tatematsu T, Sugio S, Sanbo M, Hirabayashi M, Kojima Y, Koseki J, Hosoya K, Yoshida H, Ogimoto T, Yasuda Y, Hashimoto K, Ajimizu H, Sakamori Y, Yoshida H, Sano N, Tanji M, Ito H, Terada K, Hamaji M, Menju T, Konishi H, Sato K, Kumagai S, Ghajar CM, Kato D, Date H, Yoshizawa A, Arakawa Y, Ozasa H, Moorhouse AJ, Shimamura T, Nishikawa H, Hirai T, Wake H. Microglia Display Heterogeneous Initial Responses to Disseminated Tumor Cells. Cancer Research. 2026;86(6):1414-1434. DOI: 10.1158/0008-5472.CAN-25-3425
  5. Takahashi J, Tanaka Y, Sato Y, Hashimoto H, Ueno T, Kojima S, Kuroda R, Kondo A, Okumura G, Fukagawa A, Tsuru I, Yamada Y, Takahashi S, Kojima Y, Koyama S, Ushiku T, Nishikawa H, Kume H, Mano H. Spatial Multiomic Analyses Reveal Carcinogenic Pathways in End-Stage Renal Disease. Cancer Discovery. 2026;16(3):478-496. DOI: 10.1158/2159-8290.CD-25-0472
  6. Mizukoshi C, Kojima Y (Co-Correspo), Hayashi S, Abe K, Kasugai D, Shimamura T. scSurv: a deep generative model for single-cell survival analysis. Bioinformatics. 2026;42(1):btaf671. DOI: 10.1093/bioinformatics/btaf671

国際会議論文

  1. Nishimura K, Bise R, Matsuo S, Hirose H, Kojima Y. Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2026;19801-19811. 論文ページ
  2. Shiku K, Nishimura K, Matsuo S, Kojima Y, Bise R. Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection. Proceedings of the AAAI Conference on Artificial Intelligence. 2026;40(11):9015-9023. DOI: 10.1609/aaai.v40i11.37857

2025

原著論文

  1. Nakashima T, Miyauchi T, Takeuchi R, Sugihara Y, Funakoshi Y, Ohka F, Maeda S, Hirato J, Yoshioka T, Okita H, Narita Y, Kanemura Y, Kojima Y, Watanabe Y, Saito R, Suzuki H. Diversity of U1 Small Nuclear RNAs and Diagnostic Methods for Their Mutations. Cancer Science. 2025;116(8):2270-2280. DOI: 10.1111/cas.70110
  2. Sato T, Sugiyama D, Koseki J, Kojima Y, Hattori S, Sone K, Nishinakamura H, Ishikawa T, Ishikawa Y, Kato T, Kiyoi H, Nishikawa H. Sustained inhibition of CSF1R signaling augments antitumor immunity through inhibiting tumor-associated macrophages. JCI Insight. 2025;10(1):e178146. DOI: 10.1172/jci.insight.178146

国際会議論文

  1. Nishimura K, Hirose H, Bise R, Shiku K, Kojima Y. Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics. Advances in Neural Information Processing Systems (NeurIPS 2025). 2025;38:86684-86705. 論文のDOI
  2. Nishimura K, Bise R, Kojima Y. Towards Spatial Transcriptomics-Guided Pathological Image Recognition with Batch-Agnostic Encoder. 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). 2025;1-5. DOI: 10.1109/ISBI60581.2025.10980754

総説・解説

  1. 小嶋 泰弘. 高解像度オミクスデータの深層生成モデルによる生体システムの解析. JSBi Bioinformatics Review. 2025;6(1):41-50. DOI: 10.11234/jsbibr.2025.3

2024

  1.  Kojima Y, Mii S, Hayashi S, Hirose H, Ishikawa M, Akiyama M, Enomoto A, Shimamura T,Single-cell Colocalization analysis using a generative model,Cell Syst.,Vol.15,Issue2, 180-192.e7,2024.  
  2.  Hashimoto M, Kojima Y, Sakamoto T, Ozato Y, Nakano Y, Abe T, Hosoda K, Saito H, Higuchi S, Hisamatsu Y, Toshima T, Yonemura Y, Masuda T, Hata T, Nagayama S, Kagawa K, Goto Y, Utou M, Gamachi A, Imamura K, Kuze Y, Zenkoh J, Suzuki A, Takahashi K, Niida A, Hirose H, Hayashi S, Koseki J, Fukuchi S, Murakami K, Yoshizumi T, Kadomatsu K, Tobo T, Oda Y, Uemura M, Eguchi H, Doki Y, Mori M, Oshima M, Shibata T, Suzuki Y, Shimamura T, Mimori K,Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis,EBioMedicine. 2024 Apr 9:105102. doi: 10.1016/j.ebiom.2024.105102. Epub ahead of print. PMID: 38614865.
  3. Majima K, Kojima Y (Co-Correspo), Minoura K, Abe K, Hirose H, Shimamura T, LineageVAE: Reconstructing Historical Cell States and Transcriptomes toward Unobserved Progenitors,Bioinf. https://doi.org/10.1093/bioinformatics/btae520,2024.
  4. Mizukoshi C, Kojima Y (Co-Correspo), Nomura S, Hayashi S, Abe K, Shimamura T, DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates, Genome Biol., 25, Article No.229,2024. 

2023

  1. Ishikawa M, Sugino S, Masuda Y, Tarumoto Y, Seto Y, Taniyama N, Wagai F, Yamauchi Y, Kojima Y, Kiryu H, Yusa K, Eiraku M, Mochizuki A, RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations,Commun. Biol., Vol.6, Ariticle Number1290, 2023
  2. Nakahara R, Aki S, Sugaya M, Hirose H, Kato M, Maeda K, Sakamoto D M, Kojima Y, Nishida M, Ando R, Muramatsu M, Pan M, Tsuchida R, Matsumura Y, Yanai H, Takano H, Yao R, Sando S, Shibuya M, Sakai J, Kodama T, Kidoya H, Shimamura T, Osawa T,Hypoxia activates SREBP2through Golgidisassembly in bone marrow-derived monocytesfor enhanced tumor growth,EMBO J.,DOI:10.15252/embj.2023114032
  3. Genuth M, Kojima Y(Co-First), Jülich D, Kiryu H, Holley S, Automated time-lapse data segmentation reveals in vivo cell state dynamics, Sci. Adv.,Vol.9,Issue22, DOI: 10.1126/sciadv.adf1814.,2023
  4. Koseki J, Hayashi S, Kojima Y, Hirose H, Shimamura T,  Topological data analysis of protein structure and inter/intra-molecular interaction changes attributable to amino acid mutations,Comput. Struct. Biotechnol. J., Vol.21, 2950-2959, 2023.
  5. Ozato Y, Kojima Y(Co-First), Kobayashi Y, Hisamatsu Y, Toshima T, Yonemura Y, Masuda T, Kagawa K, Goto Y, Udo M, Gamachi A, Imamura K, Kuze Y, Zenkou J, Suzuki A, Niida A, Sakamoto T, Hirose H, Hayashi S, Koseki J, Oki E, Fukuchi S, Oshima M, Tobo T, Nagayama S, Doki Y, Eguchi H, Mori M, Shibata T, Suzuki Y, Shimamura T, Mimori K, Spatial and single-cell transcriptomics decipher the cellular environment containing HLA-G+ cancer cells and SPP1+ macrophages in colorectal cancer,Cell Reports, Vol. 42, Issue 1, 111929, 2023.

2022


  1.  Nagaharu K, Kojima Y (Co-First), Hirose H, Minoura K, Hinohara K, Minami H, Kageyama Y, Sugimoto Y, Masuya M, Nii S, Seki M, Suzuki Y, Tawara I, Shimamura T, Katayama N, Nishikawa H, Ohishi K, A bifurcation concept for B-lymphoid/plasmacytoid dendritic cells with largely fluctuating transcriptome dynamics, Cell Reports, Vol.40, Issue 9, 111260, 2022.

2021

  1. Isoyama S, Mori S, Sugiyama D, Kojima Y, Tada Y, Shitara K, Hinohara K, Dan S, Nishikawa H,Cancer immunotherapy with PI3K and PD-1 dual-blockade via optimal modulation of T cell activation signal,  J. ImmunoTher. Cancer, Vol.9, Issue 8, e002279, 2021.

2020

  1. Kojima Y, Matsumoto H, Kiryu H, Estimation of population genetic parameters using an EM algorithm and sequence data from experimental evolution populations, Bioinformatics, Vol.36, Issue 1, 221-231, 2020.

2019

  1. Kiryu H, Ichikawa Y, Kojima Y, TMRS: an algorithm for computing the time to the most recent substitution event from a multiple alignment column, Algorithms Mol. Bio., Vol.14, Issue23, 2019.
計算生命科学ユニット
研究プロジェクト
研究室メンバー
論文業績
コンタクト
  • 研究概要集のバナー
  • Our Research Focusのバナー