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Found 4 papers, 2 papers with code
Deep learning tackles single-cell analysis A survey of deep learning for scRNA-seq analysis
Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline.
Convolutional neural network models for cancer type prediction based on gene expression
In breast cancer, for instance, our model identified well-known markers, such as GATA3 and ESR1.
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization
We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset.
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1. 96 (log-scale IC50 values).
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