AIMC Topic: Sequence Analysis, RNA

Clear Filters Showing 71 to 80 of 318 articles

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

International journal of molecular sciences May 29, 2024
A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pat...
Humans Machine Learning Gene Expression Profiling Transcriptome Sepsis COVID-19 Biomarkers SARS-CoV-2 Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis Sequence Analysis, RNA
View on PubMed DOI

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Journal of computational biology : a journal of computational molecular cell biology May 20, 2024
Cluster Analysis Single-Cell Analysis Computational Biology Sequence Analysis, RNA Humans Algorithms Deep Learning
View on PubMed DOI

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

Methods (San Diego, Calif.) May 18, 2024
With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devot...
RNA Processing, Post-Transcriptional Nanopore Sequencing RNA Methylation Methylation HEK293 Cells Adenosine Sequence Analysis, RNA Workflow RNA Machine Learning Algorithms Humans
View on PubMed DOI

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.

RNA biology May 17, 2024
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...
Software RNA Processing, Post-Transcriptional Transcriptome Nanopore Sequencing Methylation Computational Biology Humans HEK293 Cells Machine Learning Nanopores RNA, Messenger Sequence Analysis, RNA HeLa Cells
View on PubMed DOI

A deep learning framework for denoising and ordering scRNA-seq data using adversarial autoencoder with dynamic batching.

STAR protocols May 14, 2024
Single-cell RNA sequencing (scRNA-seq) provides high resolution of cell-to-cell variation in gene expression and offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, technical challenges such as low capt...
Humans Deep Learning Sequence Analysis, RNA Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis
View on PubMed DOI

Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing.

Scientific reports May 14, 2024
Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furth...
Humans Machine Learning Algorithms Triple Negative Breast Neoplasms Neoplastic Cells, Circulating Leukocytes, Mononuclear Female Single-Cell Analysis Breast Neoplasms Sequence Analysis, RNA Biomarkers, Tumor
View on PubMed DOI

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

Frontiers in endocrinology May 10, 2024
BACKGROUND: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and A...
Sequence Analysis, RNA Protein Interaction Maps Case-Control Studies Female Oxidative Stress RNA-Seq Humans Male Single-Cell Analysis Diabetic Retinopathy Single-Cell Gene Expression Analysis Molecular Docking Simulation Machine Learning
View on PubMed DOI

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

PloS one May 10, 2024
In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex h...
ROC Curve Sequence Analysis, RNA Support Vector Machine Decision Trees Neural Networks, Computer Machine Learning Humans Neoplasms
View on PubMed DOI

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

BMC bioinformatics May 8, 2024
BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene ex...
Sequence Analysis, RNA Transcriptome RNA-Seq Computational Biology Humans Machine Learning Neoplasms Gene Expression Profiling
View on PubMed DOI

Integrative analysis of single-cell and bulk RNA sequencing unveils a machine learning-based pan-cancer major histocompatibility complex-related signature for predicting immunotherapy efficacy.

Cancer immunology, immunotherapy : CII May 7, 2024
Major histocompatibility complex (MHC) could serve as a potential biomarker for tumor immunotherapy, however, it is not yet known whether MHC could distinguish potential beneficiaries. Single-cell RNA sequencing datasets derived from patients with im...
Humans Machine Learning Single-Cell Analysis Immunotherapy Sequence Analysis, RNA Major Histocompatibility Complex Prognosis Biomarkers, Tumor Neoplasms
View on PubMed DOI
Popular Topics
Recent Journals