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By Hong Hur on September 22, 2015.

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Essentiality, protein–protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning
Annotating cell types in single-cell ATAC data via the guidance of the underlying DNA sequences
PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors
Stroke-volume-allocation model enabling wearable sensors for vascular age and cardiovascular disease assessment
Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides

Trying RNA-Seq or NGS for the first time? Ask us anything during Illumina Sequencing Office Hours on May 16.
New RNA sequencing technology uncovers mechanism affecting generation of new COVID variants
SPADE – spatial deconvolution for domain specific cell-type estimation
MapUTR – identifies rare functional 3′ UTR variants regulating mRNA abundance of cancer driver genes
mAFiA – detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data

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