ArticleProteomics. Clinical applications2008
Evolutionary medicine: A meaningful connection between omics, disease, and treatment.
Article in Proteomics. Clinical applications, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Metabolomics of Prostate Cancer and Clinical Profiles Following Radiotherapy: Need for a Precision Phylometabolomics Approach.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- A Systems Biology Interpretation of Array Comparative Genomic Hybridization (aCGH) Data through Phylogenetics.Omics : a journal of integrative biology · 2016Article
- Computational Tools for Parsimony Phylogenetic Analysis of Omics Data.Omics : a journal of integrative biology · 2015Article
- Identifying early events of gene expression in breast cancer with systems biology phylogenetics.Cytogenetic and genome research · 2013Article
- Systems Biology Profiling of AMD on the Basis of Gene Expression.Journal of ophthalmology · 2013Article
- Cancer heterogeneity: origins and implications for genetic association studies.Trends in genetics : TIG · 2012Article
- 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
- Algorithmic assessment of vaccine-induced selective pressure and its implications on future vaccine candidates.Advances in bioinformatics · 2010Article
- Article
- Phylogenetic modeling of heterogeneous gene-expression microarray data from cancerous specimens.Omics : a journal of integrative biology · 2008Article
- The Evolution of Homeopathic Theory-Driven Research and the Methodological Toolbox.The American homeopathArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The evolutionary nature of diseases requires that their omics be analyzed by evolution-compatible analytical tools such as parsimony phylogenetics in order to reveal common mutations and pathways' modifications. Since the heterogeneity of the omics data renders some analytical tools such as phenetic clustering and Bayesian likelihood inefficient, a parsimony phylogenetic paradigm seems to connect between the omics and medicine. It offers a seamless, dynamic, predictive, and multidimensional analytical approach that reveals biological classes, and disease ontogenies; its analysis can be translated into practice for early detection, diagnosis, biomarker identification, prognosis, and assessment of treatment. Parsimony phylogenetics identifies classes of specimens, the clades, by their shared derived expressions, the synapomorphies, which are also the potential biomarkers for the classes that they delimit. Synapomorphies are determined through polarity assessment (ancestral vs. derived) of m/z or gene-expression values and parsimony analysis; this process also permits intra and interplatform comparability and produces higher concordance between platforms. Furthermore, major trends in the data are also interpreted from the graphical representation of the data as a tree diagram termed cladogram; it depicts directionality of change, identifies the transitional patterns from healthy to diseased, and can be developed into a predictive tool for early detection.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.