Unsupervised and semi‐supervised learning: The next frontier in machine learning for plant systems biology
J Yan, X Wang - The Plant Journal, 2022 - Wiley Online Library
Advances in high‐throughput omics technologies are leading plant biology research into the
era of big data. Machine learning (ML) performs an important role in plant systems biology …
era of big data. Machine learning (ML) performs an important role in plant systems biology …
Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system
The performance of decomposition-based algorithms is sensitive to the Pareto front shapes
since their reference vectors preset in advance are not always adaptable to various problem …
since their reference vectors preset in advance are not always adaptable to various problem …
Clustering single-cell RNA-seq data with a model-based deep learning approach
Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular
differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq …
differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq …
Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
Clustering is a critical step in single cell-based studies. Most existing methods support
unsupervised clustering without the a priori exploitation of any domain knowledge. When …
unsupervised clustering without the a priori exploitation of any domain knowledge. When …
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network
Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex
mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central …
mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central …
SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation
Motivation The development of single-cell RNA-sequencing (scRNA-seq) provides a new
perspective to study biological problems at the single-cell level. One of the key issues in …
perspective to study biological problems at the single-cell level. One of the key issues in …
Self-supervised contrastive learning for integrative single cell RNA-seq data analysis
We present a novel self-supervised Contrastive LEArning framework for single-cell
ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream …
ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream …
Consensus clustering of single-cell RNA-seq data by enhancing network affinity
Y Cui, S Zhang, Y Liang, X Wang… - Briefings in …, 2021 - academic.oup.com
Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-
cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised …
cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised …
A data-driven clustering recommendation method for single-cell RNA-sequencing data
Y Tian, R Zheng, Z Liang, S Li… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it
possible to solve biological problems at the single-cell resolution. One of the critical steps in …
possible to solve biological problems at the single-cell resolution. One of the critical steps in …
A riemannian admm
We consider a class of Riemannian optimization problems where the objective is the sum of
a smooth function and a nonsmooth function, considered in the ambient space. This class of …
a smooth function and a nonsmooth function, considered in the ambient space. This class of …