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Found 4 papers, 3 papers with code
Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View
In this paper, we provide a comprehensive overview of these deep learning methods, including their strengths and limitations.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Conditional out-of-sample generation for unpaired data using trVAE
While generative models have shown great success in generating high-dimensional samples conditional on low-dimensional descriptors (learning e. g. stroke thickness in MNIST, hair color in CelebA, or speaker identity in Wavenet), their generation out-of-sample poses fundamental problems.
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
Our proposed scheme, called "Deep Packet," can handle both \emph{traffic characterization} in which the network traffic is categorized into major classes (\eg, FTP and P2P) and application identification in which end-user applications (\eg, BitTorrent and Skype) identification is desired.
