AIMC Topic: RNA-Seq

Clear Filters Showing 51 to 60 of 180 articles

Generating bulk RNA-Seq gene expression data based on generative deep learning models and utilizing it for data augmentation.

Computers in biology and medicine Dec 7, 2023
Large-scale high-throughput transcriptome sequencing data holds significant value in biomedical research. However, practical challenges such as difficulty in sample acquisition often limit the availability of large sample sizes, leading to decreased ...
Reproducibility of Results Transcriptome Deep Learning RNA-Seq High-Throughput Nucleotide Sequencing
View on PubMed DOI

N-of-one differential gene expression without control samples using a deep generative model.

Genome biology Nov 16, 2023
Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can...
Machine Learning Learning RNA-Seq Gene Expression
View on PubMed DOI

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Journal of chemical information and modeling Sep 8, 2023
Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq...
Neural Networks, Computer Software Algorithms RNA-Seq
View on PubMed DOI

A scoping review on deep learning for next-generation RNA-Seq. data analysis.

Functional & integrative genomics Apr 21, 2023
In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfol...
Algorithms Reproducibility of Results Deep Learning RNA-Seq Genomics Sequence Analysis, RNA High-Throughput Nucleotide Sequencing
View on PubMed DOI

Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.

Communications biology Feb 25, 2023
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. Th...
Machine Learning Gene Expression Profiling RNA-Seq Sequence Analysis, RNA Microarray Analysis
View on PubMed DOI

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms.

Medical & biological engineering & computing Nov 10, 2022
Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics data by considering relation among genes increases the predictive performance of methods compared t...
Machine Learning Algorithms RNA-Seq
View on PubMed DOI

Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

BMC bioinformatics Sep 13, 2022
The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are cur...
Humans Gene Expression Profiling Transcriptome Deep Learning RNA-Seq
View on PubMed DOI

Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination.

Molecular omics Aug 15, 2022
RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recent studies on differential gene expression analysis and the discovery of novel cancer biomarkers have extensively used RNA-Seq data. New biomarker iden...
Humans Software Biomarkers, Tumor Neoplasms RNA-Seq Support Vector Machine
View on PubMed DOI

Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.

BioMed research international Aug 13, 2022
The cell cycle is composed of a series of ordered, highly regulated processes through which a cell grows and duplicates its genome and eventually divides into two daughter cells. According to the complex changes in cell structure and biosynthesis, th...
Humans Machine Learning Biomarkers RNA-Seq Cell Cycle Mitosis
View on PubMed DOI

Protocol to estimate cell type proportions from bulk RNA-seq using DAISM-DNN.

STAR protocols Jul 31, 2022
Computational protocols for cell type deconvolution from bulk RNA-seq data have been used to understand cellular heterogeneity in disease-related samples, but their performance can be impacted by batch effect among datasets. Here, we present a DAISM-...
Humans Neural Networks, Computer RNA-Seq
View on PubMed DOI
Popular Topics
Recent Journals