Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data

15 Feb 2019

Abstract excerpt

MOTIVATION: Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Cells and Metastasis
Share to Cancer Genomics and Diagnostics
Share to Single-cell and spatial transcriptomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 30768159
DOI: 10.1093/bioinformatics/btz095

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic dataPMID 30768159Read ↗

bmVAE: a variational autoencoder method for clustering single-cell mutation data.Semantic proximity Read ↗

Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational AutoencoderSemantic proximity Read ↗

<i>ACTIVA</i> : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencodersSemantic proximity Read ↗

A harmonized ovarian cancer scRNA-seq atlas to dissect disease heterogeneity underlying metastatization and chemoresistanceSemantic proximity Read ↗

ISMI-VAE: A Deep Learning Model for Classifying Disease Cells Using Gene Expression and SNV DataSemantic proximity Read ↗

ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data.Semantic proximity Read ↗

SIDISH Identifies High-Risk Disease-Associated Cells and Biomarkers by Integrating Single-Cell Depth and Bulk BreadthSemantic proximity Read ↗

Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAESemantic proximity Read ↗

Designing Single Cell RNA-Sequencing Experiments for Learning Latent RepresentationsSemantic proximity Read ↗

ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell dataSemantic proximity Read ↗

A Bayesian method to cluster single-cell RNA sequencing data using Copy Number AlterationsSemantic proximity Read ↗

Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.