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Evolutionary medicine: A meaningful connection between omics, disease, and treatment
Author(s) -
AbuAsab Mones,
Chaouchi Mohamed,
Amri Hakima
Publication year - 2008
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.200780047
Subject(s) - synapomorphy , cladogram , biology , concordance , identification (biology) , phylogenetics , phylogenetic tree , computational biology , clade , evolutionary biology , bioinformatics , computer science , data mining , genetics , gene , ecology
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.