Iterative signature algorithm for the analysis of large-scale gene expression data.
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Summary
It is shown analytically that for noisy expression data the proposed approach leads to better classification due to the implementation of the threshold, and argues that the method is in fact a generalization of singular value decomposition, which corresponds to the special case where no threshold is applied.
- Type
- article
- Published
- 2002-10-08
- Cited by
- 485
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W2080278541
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3053509
Keywords
Computer science, Algorithm, Expression (computer science), Singular value decomposition, Data mining
References
- Pattern Classification
- Array of hope
- IL-13受体α2降低血吸虫病肉芽肿的炎症反应并延长宿主存活时间[英]/Mentink-Kane MM,Cheever AW，Thompson RW，et al//Proc Natl Acad Sci U S A
- Biclustering of Expression Data
- Molecular classification of cutaneous malignant melanoma by gene expression profiling
- Learning the parts of objects by non-negative matrix factorization
- Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray
- Whole-genome expression analysis: challenges beyond clustering.
- A gene expression database for the molecular pharmacology of cancer
- Gene expression data analysis
- A semidiscrete matrix decomposition for latent semantic indexing information retrieval
- Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
- Data analysis and integration: of steps and arrows
- Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.
- Fundamental patterns underlying gene expression profiles: simplicity from complexity.
- Chemosensitivity prediction by transcriptional profiling
- Systematic determination of genetic network architecture
- Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.
- Coupled two-way clustering analysis of gene microarray data.
- Navigating gene expression using microarrays — a technology review
Cited by
- Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae
- WF-MSB: A weighted fuzzy-based biclustering method for gene expression data
- Arboretum: Reconstruction and analysis of the evolutionary history of condition-specific transcriptional modules
- Computational ortholog prediction: evaluating use cases and improving high-throughput performance
- Integrated Inference and Analysis of Regulatory Networks from Multi-Level Measurements
- Stable Biclustering of Gene Expression Data with Nonnegative Matrix Factorizations
- Mib: Using Mutual Information for Biclustering High Dimensional Data
- Evolution, Modularity, and Dynamics of Gene Regulatory Networks
- Découvertes de motifs pertinents pour l'analyse du transcriptome : application à l'insulino-résistance
- Sisa: Seeded Iterative Signature Algorithm for Biclustering Gene Expression Data
- TOWARDS A MOLECULAR-LEVEL UNDERSTANDING OF BIOLOGICAL PROCESSES BY UNSUPERVISED ANALYSIS OF GENE EXPRESSION DATA
- Comparisons of predicted genetic modules: identification of co-expressed genes through module gene flow.
- Global transcription profiles of C. albicans and their comparison with other yeast species.
- Attractor Molecular Signatures and Their Applications for Prognostic Biomarkers
- A New Co-similarity Measure : Application to Text Mining and Bioinformatics. (Une Nouvelle Mesure de Co-Similarité : Applications aux Données Textuelles et Génomique)
- Diagnostics using SELDI-TOF Mass Spectrometry
- Mining large collections of gene expression data to elucidate transcriptional regulation of biological processes
- Scatter search-based identification of local patterns with positive and negative correlations in gene expression data
- Characterization of Conserved Toxicogenomic Responses in Chemically Exposed Hepatocytes across Species and Platforms
- Big Data Analytics in Bioinformatics: A Machine Learning Perspective
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