Cell identi cation from scRNA-seq data with Variational Autoencoders

A large portion of the information contained in next-generation sequencing data is potentially lost through classical bioinformatics analysis.







learning models for prognosis prediction using multi-omics data
The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.
Interpretable Machine Learning for Genomics | Research Square
Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum ...
The Importance of Data Sources for Machine Learning Applications ...
Our study starts with the read count matrices. Because of the low initial amounts of RNA obtained from every single cell, the discrete count data matrix output.
Design and implementation of bioinformatic tools for RNA ...
In addition to learning to recognize patterns in DNA sequences, machine learning can take as input data generated by other genomic assays, such as microarray or ...
Machine learning applications in genetics and genomics - SciSpace
Specifically, development of generative AI models that generate realistic, representative scRNA-seq data from bulk RNA-seq data would enable ...
Generating Synthetic Single Cell Data from Bulk RNA-seq ... - bioRxiv
Résumé : Cette thèse traite de la modélisation et de l'analyse de don- nées de comptage de haute dimension.
Machine learning for multivariate analysis of high-dimensional count ...
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