Current State-of-the-Art of Clustering Methods for Gene Expression Data with RNA-Seq

Latest developments in high-throughput cDNA sequencing (RNA-seq) have revolutionized gene expression profiling. This analysis aims to compare the expression levels of multiple genes between two or more samples, under specific circumstances or in a specific cell to give a global picture of cellular f...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jamail, Ismail, Moussa, Ahmed
Formato:	Capítulo de libro
Lenguaje:	inglés
Publicado:	IntechOpen 2020
Materias:	Physical Sciences, Engineering and Technology
Acceso en línea:	https://www.intechopen.com/chapters/73567
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares: Current State-of-the-Art of Clustering Methods for Gene Expression Data with RNA-Seq

Vista rápida
Computational methods for differentially expressed gene analysis from RNA-Seq: an overview
Vista rápida
Gene Expression Analysis Using RNA-Seq from Organisms Lacking Substantial Genomic Resources
Vista rápida
Enhanced Single-Cell RNA-seq Embedding through Gene Expression and Data-Driven Gene-Gene Interaction Integration
Vista rápida
Optirank: classification for RNA-Seq data with optimal ranking reference genes
Vista rápida
SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
Vista rápida
scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning
Vista rápida
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Vista rápida
Ensemble Classification and Cancer-Specific Gene Signatures from Quality-Aware Hybrid Gene Selection in TCGA RNA-Seq data
Vista rápida
Bridging Graph and Pairwise Learning for Gene Regulatory Network Inference from scRNA-seq Data
Vista rápida
Federated Generation of Synthetic RNA-seq Data
Vista rápida
Modulation of Gene Expression by RNA Binding Proteins: mRNA Stability and Translation
Vista rápida
A sparse negative binomial mixture model for clustering RNA-seq count data
Vista rápida
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization
Vista rápida
Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data
Vista rápida
RNA Polymerase II Phosphorylation and Gene Expression Regulation
Vista rápida
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data
Vista rápida
scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data
Vista rápida
Predicting Gene Disease Associations in Type 2 Diabetes Using Machine Learning on Single-Cell RNA-Seq Data
Vista rápida
Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
Vista rápida
On de novo Bridging Paired-end RNA-seq Data
Vista rápida
Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification
Vista rápida
Online t-SNE for single-cell RNA-seq
Vista rápida
RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection
Vista rápida
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE