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Exploring Single-Cell Data with Deep Multitasking Neural Networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
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Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
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An Immune Atlas of Clear Cell Renal Cell Carcinoma
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Defining a Cancer Dependency Map
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Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment
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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
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Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks
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Manifold learning-based methods for analyzing single-cell RNA-sequencing data
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CyTOF supports efficient detection of immune cell subsets from small samples
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A Continuous Molecular Roadmap to iPSC Reprogramming through Progression Analysis of Single-Cell Mass Cytometry
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Iterative Procrustes alignment with the EM algorithm
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Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
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Wishbone identifies bifurcating developmental trajectories from single-cell data
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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
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Diffusion pseudotime robustly reconstructs lineage branching
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Junctional sequences influence the specificity of gamma/delta T cell receptors.
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Removal of batch effects using distribution-matching residual networks
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Adjusting batch effects in microarray expression data using empirical Bayes methods
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Shannon and von Neumann entropy of random networks with heterogeneous expected degree
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Leveraging Stacked Denoising Autoencoder in Prediction of Pathogen-Host Protein-Protein Interactions
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Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction
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Matrix-based Kernel Principal Component analysis for large-scale data set
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Representation Learning: A Review and New Perspectives
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Reducing the Dimensionality of Data with Neural Networks
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Nonlinear Dimensionality Reduction by Locally Linear Embedding
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Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
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Antibody-dependent enhancement of severe dengue disease in humans
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Detection and localization of surgically resectable cancers with a multi-analyte blood test
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ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions
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Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders
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γδ T Cells: First Line of Defense and Beyond
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High CD123 levels enhance proliferation in response to IL-3, but reduce chemotaxis by downregulating CXCR4 expression
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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The landscape of cell lineage tracing
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Advances and applications in single-cell and spatial genomics
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Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models
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Predicting single cell genotypes from single cell expression profiles in AML using deep learning
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