The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data
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Summary
This work proposes the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays.
- Type
- article
- Published
- 2007-05-15
- Cited by
- 16
- References
- 30
- Access
- Open access
- OpenAlex
- https://openalex.org/W2013295248
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:19015405
Keywords
Computer science, Data mining, Wavelet, Expression (computer science), Temporal database
References
- DNA microarrays. History and overview.
- Genetic networks. Small numbers of big molecules.
- Wavelet to predict bacterial ori and ter: a tendency towards a physical balance
- Transcriptional regulatory networks and the yeast cell cycle.
- Continuous Representations of Time-Series Gene Expression Data
- Analysis of gene expression data using self‐organizing maps
- Serial analysis of gene expression (SAGE): advances, analysis and applications to pigment cell research.
- Functional genomics as applied to mapping transcription regulatory networks.
- Ordering Genes in a Flagella Pathway by Analysis of Expression Kinetics from Living Bacteria
- Using Bayesian Networks to Analyze Expression Data
- Modulation of Pseudomonas aeruginosa gene expression by host microflora through interspecies communication
- Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.
- Analyzing time series gene expression data
- Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes
- Aligning gene expression time series with time warping algorithms
- A genome-wide transcriptional analysis of the mitotic cell cycle.
- Navigating gene expression using microarrays — a technology review
- Cluster analysis and display of genome-wide expression patterns.
- Fluctuations and slow variables in genetic networks.
- Wavelets in bioinformatics and computational biology: state of art and perspectives
Cited by
- Some methods and models for analyzing time-series gene expression data
- Characterizing Gene Expressions Based on Their Temporal Observations
- Inferring nonstationary gene networks from temporal gene expression data
- Inferring Nonstationary Gene Networks from Longitudinal Gene Expression Microarrays
- Analysis of air quality monitoring networks by functional clustering
- Autocorrelation-based fuzzy clustering of time series
- A Genome-Wide Analysis of Array-Based Comparative Genomic Hybridization (CGH) Data to Detect Intra-Species Variations and Evolutionary Relationships
- Quantifying periodicity in omics data
- Dynamic Clustering of Gene Expression
- Time resolved chromatin architecture and transcription regulation during the yeast respiratory cycle
- Analysis of time course Omics datasets.
- Using Wavelet Analysis To Assist in Identification of Significant Events in Molecular Dynamics Simulations
- An accurate and rapid continuous wavelet dynamic time warping algorithm for unbalanced global mapping in nanopore sequencing
- An accurate and rapid continuous wavelet dynamic time warping algorithm for end‐to‐end mapping in ultra‐long nanopore sequencing
- Topological Data Analysis for Genomics and Evolution: Topology in Biology
- Transcriptomics Methodology for analyzing temporal patterns of differential coexpression using meta-analysis
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