Tag Archives: deep learning

Next-generation sequencing (NGS) technology has become a powerful tool for dissecting the molecular and pathological signatures of a variety of human...

Nanopore sequencing enables direct measurement of RNA molecules without conversion to cDNA, thus opening the gates to a new era for RNA biology...

Single-cell RNA sequencing (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these “doublets” violate the fundamental premise of single-cell ...

The development of single cell transcriptome sequencing has allowed researchers the possibility to dig inside the role of the individual cell types in a plethora of disease scenarios. It also expands to the whole transcriptome what before was only possible ...

Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. Researchers at the University of Hawaii present DeepImpute, a deep neural network-based imputation...

Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks...

A research team at Children’s Hospital of Philadelphia (CHOP) has developed an innovative computational tool offering researchers an efficient method for detecting the different ways RNA is pieced together (spliced) when copied from DNA...

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Illumina scientists...

Researchers at Oregon State University have used deep learning to decipher which ribonucleic acids have the potential to encode proteins. The gated recurrent neural network developed in the College...