Search Results for author: Xiucai Ye

Found 8 papers, 5 papers with code

MoFormer: Multi-objective Antimicrobial Peptide Generation Based on Conditional Transformer Joint Multi-modal Fusion Descriptor

no code implementations3 Jun 2024 Li Wang, Xiangzheng Fu, Jiahao Yang, Xinyi Zhang, Xiucai Ye, Yiping Liu, Tetsuya Sakurai, Xiangxiang Zeng

Deep learning holds a big promise for optimizing existing peptides with more desirable properties, a critical step towards accelerating new drug discovery.

Drug Discovery

Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGS

1 code implementation15 Nov 2022 Meng Huang, Xiucai Ye, Tetsuya Sakurai

In this paper, to unveil interpretable development-specific gene signatures in human PFC, we propose a novel gene selection method, named Interpretable Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to represent causality between multiple gene variables and a development variable.

Contrastive Learning

Inferring cell-specific lncRNA regulation with single-cell RNA-sequencing data in the developing human neocortex

1 code implementation15 Nov 2022 Meng Huang, Jiangtao Ma, Changzhou Long, Junpeng Zhang, Xiucai Ye, Tetsuya Sakurai

However, to analyze lncRNA regulation regarding individual cells, we focus on single-cell RNA-sequencing (scRNA-seq) data instead of bulk data.

LSEC: Large-scale spectral ensemble clustering

1 code implementation18 Jun 2021 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

In LSEC, a large-scale spectral clustering based efficient ensemble generation framework is designed to generate various base clusterings within a low computational complexity.

Clustering

Divide-and-conquer based Large-Scale Spectral Clustering

1 code implementation30 Apr 2021 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness.

Clustering Image/Document Clustering

Ensemble Learning for Spectral Clustering

1 code implementation20 Nov 2020 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

Instead of directly using the clustering results obtained from each base spectral clustering algorithm, the proposed method learns a robust presentation of graph Laplacian by ensemble learning from the spectral embedding of each base spectral clustering algorithm.

Clustering Ensemble Learning +1

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