AIMC Topic: RNA-Seq

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Unsupervised generative and graph representation learning for modelling cell differentiation.

Scientific reports
Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allow...

Machine learning uncovers cell identity regulator by histone code.

Nature communications
Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and t...

Jupyter notebook-based tools for building structured datasets from the Sequence Read Archive.

F1000Research
The Sequence Read Archive (SRA) is a large public repository that stores raw next-generation sequencing data from thousands of diverse scientific investigations.  Despite its promise, reuse and re-analysis of SRA data has been challenged by the heter...

Putative cell type discovery from single-cell gene expression data.

Nature methods
We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells...

Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-Seq Data.

Genes
Single-cell RNA-seq (scRNA-seq) is quite prevalent in studying transcriptomes, but it suffers from excessive zeros, some of which are true, but others are false. False zeros, which can be seen as missing data, obstruct the downstream analysis of sing...

Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis.

Nature communications
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding a...

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.

BMC medical genomics
BACKGROUND: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer pat...

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

Genomics
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understan...