Article

Unsupervised machine learning reveals temporal components of gene expression in HeLa cells following release from cell cycle arrest

2022-07-14

Abstract excerpt

<h4>ABSTRACT</h4> Gene expression measurements of tissues, tumors, or cell lines taken over multiple time points are valuable for describing dynamic biological phenomena such as the response to growth factors. However, such phenomena typically involve multiple biological processes occurring in parallel, making it difficult to identify and discern their respective contributions at any time point. Here, we demonstr...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: a39114fe-a19c-5007-8a2c-aa32b2c85402
DOI: 10.1101/2022.07.13.499875

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Unsupervised machine learning reveals temporal components of gene expression in HeLa cells following release from cell cycle arrestDOI 10.1101/2022.07.13.499875Read

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approachSemantic proximity Read

Time-series RNA-Seq and data-driven network inference unveil dynamics of cell activation, survival and crosstalk in Chronic Lymphocytic Leukaemia in vitro modelsSemantic proximity Read

EPIGENE: genome wide transcription unit annotation using a multivariate probabilistic model of histone modificationsSemantic proximity Read

GeneRhythm uncovers gene expression rhythms that shape the dynamic transcriptional landscape in single-cell omicsSemantic proximity Read

TopicNet: a framework for measuring transcriptional regulatory network changeSemantic proximity Read

CoPhaser: generic modeling of biological cycles in scRNA-seq with context-dependent periodic manifoldsSemantic proximity Read

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systemsSemantic proximity Read

Nascent RNA sequencing reveals a dynamic global transcriptional response at genes and enhancers to the natural medicinal compound celastrolSemantic proximity Read

Impulse model-based differential expression analysis of time course sequencing dataSemantic proximity Read

Regulation of transcription reactivation dynamics exiting mitosisSemantic proximity Read

Clustering gene expression time series data using an infinite Gaussian process mixture modelSemantic proximity Read

Estimation of nonlinear gene regulatory networks via L1 regularized NVAR from time series gene expression data.Semantic proximity Read