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Sequence Analysis, RNA

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

Frontiers in endocrinology 38800474
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...
Case-Control Studies Diabetic Retinopathy Female Humans Machine Learning Male Molecular Docking Simulation Oxidative Stress Protein Interaction Maps RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis
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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 38758523
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...
Computational Biology HEK293 Cells HeLa Cells Humans Machine Learning Methylation Nanopore Sequencing Nanopores RNA Processing, Post-Transcriptional RNA, Messenger Sequence Analysis, RNA Software Transcriptome
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SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell genomics 38823397
Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-effi...
Animals Brain Cell Differentiation Deep Learning Humans Mice Neurons Organoids Sequence Analysis, RNA Single-Cell Analysis
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Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

Methods (San Diego, Calif.) 38768930
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...
Adenosine Algorithms HEK293 Cells Humans Machine Learning Methylation Nanopore Sequencing RNA RNA Methylation RNA Processing, Post-Transcriptional Sequence Analysis, RNA Workflow
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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 38758925
Algorithms Cluster Analysis Computational Biology Deep Learning Humans Sequence Analysis, RNA Single-Cell Analysis
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NNICE: a deep quantile neural network algorithm for expression deconvolution.

Scientific reports 38890415
The composition of cell-type is a key indicator of health. Advancements in bulk gene expression data curation, single cell RNA-sequencing technologies, and computational deconvolution approaches offer a new perspective to learn about the composition ...
Algorithms Computational Biology Deep Learning Gene Expression Profiling Humans Neural Networks, Computer RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling.

Analytical chemistry 38874444
RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information...
Epigenesis, Genetic High-Throughput Nucleotide Sequencing Humans Machine Learning Quantum Theory RNA Sequence Analysis, RNA
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DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.

Scientific reports 38866896
Single-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data,...
Algorithms Cluster Analysis Humans Machine Learning RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis
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Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage.

International immunopharmacology 38865753
BACKGROUND: Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH.
Algorithms Animals Cerebral Hemorrhage Disease Models, Animal Humans Machine Learning Male Mice Microglia Monocytes Oxidative Stress Sequence Analysis, RNA Single-Cell Analysis
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Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8 + T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine learning: bioinformatics multi-omics study.

International journal of surgery (London, England) 38759695
BACKGROUND: Cancer-associated fibroblasts (CAFs) are found in primary and advanced tumours. They are primarily involved in tumour progression through complex mechanisms with other types of cells in the tumour microenvironment. However, essential fibr...
Cancer-Associated Fibroblasts CD8-Positive T-Lymphocytes Computational Biology Female Humans Immunotherapy Machine Learning Male Multiomics Prognosis Sequence Analysis, RNA Single-Cell Analysis Transcriptome Tumor Microenvironment Urinary Bladder Neoplasms
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