ArticleOmics : a journal of integrative biology2008
Phylogenetic modeling of heterogeneous gene-expression microarray data from cancerous specimens.
Article in Omics : a journal of integrative biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Metabolomics of Prostate Cancer and Clinical Profiles Following Radiotherapy: Need for a Precision Phylometabolomics Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Metabolomics-Based Biosignatures of Prostate Cancer in Patients Following Radiotherapy.Omics : a journal of integrative biology · 2019Article
- Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.Omics : a journal of integrative biology · 2017Article
- PhyloOncology: Understanding cancer through phylogenetic analysis.Biochimica et biophysica acta. Reviews on cancer · 2017Review
- Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis.Omics : a journal of integrative biology · 2016Article
- Computational Tools for Parsimony Phylogenetic Analysis of Omics Data.Omics : a journal of integrative biology · 2015Article
- Systems Biology Profiling of AMD on the Basis of Gene Expression.Journal of ophthalmology · 2013Article
- Identifying early events of gene expression in breast cancer with systems biology phylogenetics.Cytogenetic and genome research · 2013Article
- Analyzing heterogeneous complexity in complementary and alternative medicine research: a systems biology solution via parsimony phylogenetics.Forschende Komplementarmedizin (2006) · 2012Article
- Biomarkers in the age of omics: time for a systems biology approach.Omics : a journal of integrative biology · 2011Article
- Endometriosis gene expression heterogeneity and biosignature: a phylogenetic analysis.Obstetrics and gynecology international · 2011Article
- Beyond microarrays.Science signaling · 2008Article
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Authors and funding
3 authors.
Funding
Abstract
The qualitative dimension of gene expression data and its heterogeneous nature in cancerous specimens can be accounted for by phylogenetic modeling that incorporates the directionality of altered gene expressions, complex patterns of expressions among a group of specimens, and data-based rather than specimen-based gene linkage. Our phylogenetic modeling approach is a double algorithmic technique that includes polarity assessment that brings out the qualitative value of the data, followed by maximum parsimony analysis that is most suitable for the data heterogeneity of cancer gene expression. We demonstrate that polarity assessment of expression values into derived and ancestral states, via outgroup comparison, reduces experimental noise; reveals dichotomously expressed asynchronous genes; and allows data pooling as well as comparability of intra- and interplatforms. Parsimony phylogenetic analysis of the polarized values produces a multidimensional classification of specimens into clades that reveal shared derived gene expressions (the synapomorphies); provides better assessment of ontogenic pathways and phyletic relatedness of specimens; efficiently utilizes dichotomously expressed genes; produces highly predictive class recognition; illustrates gene linkage and multiple developmental pathways; provides higher concordance between gene lists; and projects the direction of change among specimens. Further implication of this phylogenetic approach is that it may transform microarray into diagnostic, prognostic, and predictive tool.
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