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

Pattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study.

Bioinformatics (Oxford, England) - 1 Jul 2015

Hendrickx Diana M, Jennen Danyel G J, Briedé Jacob J, Cavill Rachel, de Kok Theo M, Kleinjans Jos C S

Abstract excerpt

MOTIVATION: Comparing time courses of gene expression with time courses of phenotypic data may provide new insights in cellular mechanisms. In this study, we compared the performance of five pattern recognition methods with respect to their ability to relate genes and phenotypic data: one classical method (k-means) and four methods especially developed for time series [Short Time-series Expression Miner (STEM),...

Read the complete abstract on PubMed

Topics

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

Share to Benzo(a)pyrene
Share to Carcinoma, Hepatocellular

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Linear Models

Share to Liver Neoplasms

Share to Pattern Recognition, Automated

Share to Software

Share to Vitamin K 3

Source

Identifiers

PMID: 25701576
DOI: 10.1093/bioinformatics/btv108

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Pattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study.PMID 25701576Read ↗

Shape-constrained, changepoint additive models for time series omics data with cpamSemantic proximity Read ↗

scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seqSemantic proximity Read ↗

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

TEMPO: Detecting Pathway-Specific Temporal Dysregulation of Gene Expression in DiseaseSemantic proximity Read ↗

Cluster-Rasch models for microarray gene expression data.Semantic proximity Read ↗

Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fractionSemantic proximity Read ↗

Time Series Analysis of Benzo[A]Pyrene-Induced Transcriptome Changes Suggests That a Network of Transcription Factors Regulates the Effects on Functional Gene SetsSemantic proximity Read ↗

A method for detection of differential gene expression in the presence of inter-individual variability in response.Semantic proximity Read ↗

VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data.Semantic proximity Read ↗

Learning perturbation-inducible cell states of novel compounds from observability analysis of transcriptome dynamicsSemantic proximity Read ↗

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

Systematic comparison of temporal transcriptional responses to hepatotoxicants in primary human hepatocytes and HepaRG cells using concentration-response modelling of gene co-expression networksSemantic proximity Read ↗

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