Cargando…

Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data

BACKGROUND: The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wang, Juan, Lu, Cong-Hai, Kong, Xiang-Zhen, Dai, Ling-Yun, Yuan, Shasha, Zhang, Xiaofeng
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2022
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8772048/ https://www.ncbi.nlm.nih.gov/pubmed/35057729 http://dx.doi.org/10.1186/s12859-021-04220-6

Ejemplares similares

Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints
por: Wang, Juan, et al.
Publicado: (2019)

Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function
por: Lu, Conghai, et al.
Publicado: (2020)

Multi-omic and multi-view clustering algorithms: review and cancer benchmark
por: Rappoport, Nimrod, et al.
Publicado: (2019)

Multi-omic and multi-view clustering algorithms: review and cancer benchmark
por: Rappoport, Nimrod, et al.
Publicado: (2018)

Multi-view clustering for multi-omics data using unified embedding
por: Mitra, Sayantan, et al.
Publicado: (2020)

Dynamic guided metric representation learning for multi-view clustering
por: Zheng, Tingyi, et al.
Publicado: (2022)

Low-rank graph optimization for multi-view dimensionality reduction
por: Qian, Youcheng, et al.
Publicado: (2019)

Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages
por: Zhang, Enli, et al.
Publicado: (2018)

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction
por: Liu, Jian, et al.
Publicado: (2021)

Metabolomics and the Multi-Omics View of Cancer
por: Wishart, David
Publicado: (2022)

Semi-supervised multi-label classification using an extended graph-based manifold regularization
por: Li, Ding, et al.
Publicado: (2022)

Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data
por: Shi, Qianqian, et al.
Publicado: (2019)

Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona
por: Cao, Kai, et al.
Publicado: (2021)

Compact manifolds with special holonomy
por: Joyce, Dominic D
Publicado: (2000)

MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning
por: Chen, Yuxin, et al.
Publicado: (2023)

Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data
por: Yu, Na, et al.
Publicado: (2019)

On the geometric and the algebraic rank of graph manifolds
por: Schultens, J, et al.
Publicado: (2002)

Multi-omic data integration enables discovery of hidden biological regularities
por: Ebrahim, Ali, et al.
Publicado: (2016)

Robust auto-weighted multi-view subspace clustering with common subspace representation matrix
por: Zhuge, Wenzhang, et al.
Publicado: (2017)

Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding
por: Wu, Jianping, et al.
Publicado: (2019)

A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection
por: Liu, Qi
Publicado: (2022)

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features
por: Xie, Gangcai, et al.
Publicado: (2019)

Compact homogeneous Leviflat CR-manifolds
por: Al-Abdallah, A. R., et al.
Publicado: (2021)

Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification
por: Ye, Qing, et al.
Publicado: (2015)

High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis
por: Dong, Aimei, et al.
Publicado: (2021)

Causal Learning via Manifold Regularization
por: Hill, Steven M., et al.
Publicado: (2019)

A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency
por: Ye, Xulun, et al.
Publicado: (2018)

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification
por: Wu, Dingming, et al.
Publicado: (2015)

Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis
por: Revilla, Lluís, et al.
Publicado: (2021)

Clustering of cancer data based on Stiefel manifold for multiple views
por: Tian, Jing, et al.
Publicado: (2021)

Co-differential Gene Selection and Clustering Based on Graph Regularized Multi-View NMF in Cancer Genomic Data
por: Yu, Na, et al.
Publicado: (2018)

Fuchsian Inequality On A Compact Kähler Manifold
por: Bolibruch, A
Publicado: (2000)

On symplectomorphisms of the symplectisation of a compact contact manifold
por: Banyaga, A
Publicado: (2004)

Classification of algebraic varieties and compact complex manifolds
por: Popp, Herbert
Publicado: (1974)

Omics community detection using multi-resolution clustering
por: Rahnavard, Ali, et al.
Publicado: (2021)

Consensus clustering applied to multi-omics disease subtyping
por: Brière, Galadriel, et al.
Publicado: (2021)

Manifold regularization for sparse unmixing of hyperspectral images
por: Liu, Junmin, et al.
Publicado: (2016)

A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering
por: Zhu, Rong, et al.
Publicado: (2017)

Information Geometry of the Retinal Representation Manifold
por: Ding, Xuehao, et al.
Publicado: (2023)

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation
por: Yang, Guoliang, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_asn6fco5ncppsbmq9frlnaqgpl