Imoto S - Search Results

1

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.

Xu J, Li F, Li C, Guo X, Landersdorfer C, Shen HH, Peleg AY, Li J, Imoto S, Yao J, Akutsu T, Song J. Xu J, et al. Among authors: imoto s. Brief Bioinform. 2023 Jul 20;24(4):bbad240. doi: 10.1093/bib/bbad240. Brief Bioinform. 2023. PMID: 37369638 Free PMC article.

2

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

Pan T, Li C, Bi Y, Wang Z, Gasser RB, Purcell AW, Akutsu T, Webb GI, Imoto S, Song J. Pan T, et al. Among authors: imoto s. Bioinformatics. 2023 Mar 1;39(3):btad094. doi: 10.1093/bioinformatics/btad094. Bioinformatics. 2023. PMID: 36794913 Free PMC article.

3

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

Wang Z, Bi Y, Pan T, Wang X, Bain C, Bassed R, Imoto S, Yao J, Daly RJ, Song J. Wang Z, et al. Among authors: imoto s. Bioinformatics. 2023 Mar 1;39(3):btad114. doi: 10.1093/bioinformatics/btad114. Bioinformatics. 2023. PMID: 36864612 Free PMC article.

4

Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning.

Zhang YZ, Liu Y, Bai Z, Fujimoto K, Uematsu S, Imoto S. Zhang YZ, et al. Among authors: imoto s. Brief Bioinform. 2023 Sep 20;24(5):bbad239. doi: 10.1093/bib/bbad239. Brief Bioinform. 2023. PMID: 37466138 Free PMC article.

5

Investigation of the BERT model on nucleotide sequences with non-standard pre-training and evaluation of different k-mer embeddings.

Zhang YZ, Bai Z, Imoto S. Zhang YZ, et al. Among authors: imoto s. Bioinformatics. 2023 Oct 3;39(10):btad617. doi: 10.1093/bioinformatics/btad617. Bioinformatics. 2023. PMID: 37815839 Free PMC article.

6

ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics.

Sato N, Uematsu M, Fujimoto K, Uematsu S, Imoto S. Sato N, et al. Among authors: imoto s. Bioinformatics. 2023 Oct 3;39(10):btad622. doi: 10.1093/bioinformatics/btad622. Bioinformatics. 2023. PMID: 37846038 Free PMC article.

7

IVEA: an integrative variational Bayesian inference method for predicting enhancer-gene regulatory interactions.

Kimura Y, Ono Y, Katayama K, Imoto S. Kimura Y, et al. Among authors: imoto s. Bioinform Adv. 2024 Aug 20;4(1):vbae118. doi: 10.1093/bioadv/vbae118. eCollection 2024. Bioinform Adv. 2024. PMID: 39193566 Free PMC article.

8

Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases.

Liu Y, Zhang YZ, Imoto S. Liu Y, et al. Among authors: imoto s. PLoS One. 2023 Aug 21;18(8):e0290307. doi: 10.1371/journal.pone.0290307. eCollection 2023. PLoS One. 2023. PMID: 37603579 Free PMC article.

9

Genome analysis through image processing with deep learning models.

Zhang YZ, Imoto S. Zhang YZ, et al. Among authors: imoto s. J Hum Genet. 2024 Oct;69(10):519-525. doi: 10.1038/s10038-024-01275-0. Epub 2024 Jul 31. J Hum Genet. 2024. PMID: 39085457 Free PMC article. Review.

10

The transcriptome signature analysis of the epithelial-mesenchymal transition and immune cell infiltration in colon adenocarcinoma.

Heryanto YD, Imoto S. Heryanto YD, et al. Among authors: imoto s. Sci Rep. 2023 Oct 26;13(1):18383. doi: 10.1038/s41598-023-45792-y. Sci Rep. 2023. PMID: 37884639 Free PMC article.

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