A5101498383- Chinese history and philosophy
- Single-cell and spatial transcriptomics
- Legal principles and applications
- Legal Education and Practice Innovations
- Cancer-related molecular mechanisms research
- Gene expression and cancer classification
- Translation Studies and Practices
- Asian Culture and Media Studies
- Comparative and International Law Studies
- Conflict of Laws and Jurisdiction
- Educational Technology and Pedagogy
- Law and Political Science
- Extracellular vesicles in disease
- Linguistic, Cultural, and Literary Studies
- Religious Studies and Spiritual Practices
- European and International Contract Law
- Consumer Perception and Purchasing Behavior
- Bioinformatics and Genomic Networks
- Cell Image Analysis Techniques
- Linguistics and Language Analysis
- Microfluidic and Bio-sensing Technologies
- Innovative Educational Techniques
- Medical Imaging and Analysis
- Face and Expression Recognition
- Domain Adaptation and Few-Shot Learning
Hunan University
2006-2025
Harbin Engineering University
2024
Central South University
2013
Yunnan Nationalities University
2009-2012
Yunnan University
2012
Xi'an International Studies University
2009
Soochow University
2007-2008
Renmin University of China
2007-2008
Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) is widely used to reveal cellular heterogeneity, complex disease mechanisms and cell differentiation processes. Due high sparsity gene expression patterns, scRNA-seq data present a large number of dropout events, affecting downstream tasks such as clustering pseudo-time analysis. Restoring the levels genes essential for reducing technical noise facilitating However, existing imputation methods ignore topological structure information...
Cell-type annotation plays a crucial role in single-cell RNA-seq (scRNA-seq) data analysis. As more and well-annotated scRNA-seq reference are publicly available, automatical label transference algorithms gaining popularity over manual marker gene-based methods. However, most existing methods fail to unify cell-type with dimensionality reduction unable generate deep latent representation from the perspective of generation.In this article, we propose scSemiGAN, semi-supervised framework based...
single-cell RNA-sequencing (scRNA-seq) technology can reveal cellular heterogeneity with high throughput and resolution, facilitating the profiling of transcriptomes. However, due to some experimental factors, a large number missing values are generated in scRNA-seq data, which called dropout events, this phenomenon affects downstream analysis. Imputation is an effective denoising method, but existing imputation methods still face huge challenge: lack interpretability. In study, we propose...