Current Issues in Biomedical Research

dc.contributor.authorMones Abu-Asab
dc.date.accessioned2017-05-03T09:36:08Z
dc.date.available2017-05-03T09:36:08Z
dc.date.issued2010-09-26
dc.description.abstract<p>The advent of omics techniques, such as microarrays and mass spectrometry of metabolomicsand proteomics, and their generated data avalanche has brought in an added level ofcomplexity to biomedical research: mining massively heterogeneous data for biologicalsignificance. Two major issues have become the source of “toxic assets” in biomedicalresearch; the first is the researchers’ underestimation (or lack of awareness) of populationalheterogeneity, and the other is the use of unsuitable analytical bioinformatic paradigms indealing with heterogeneous data. Consequently, we are still struggling with many standingissues such as disease definition and its molecular boundaries, class discovery (i.e., subtypingof disease), early detection, susceptibility to drug side effects, omics biomarkers discovery,specimen profiling (from genetic, proteomic, metabolomic…etc), sorting out of clonal fromnon-expanded mutations, genetic versus epigenetic driving events, post-treatment assessment,and figuring out the primary origin of some cancers.Methods using statistical averaging have their limitations since they are unsuitable for theanalysis of heterogeneity; they hide intrapopulational diversity and homogenizes otherwiseheterogeneous subpopulations. Many researchers are unaware of alternative methods ofanalysis that take into account individual variations, and consequently there is a tremendouswaste of resources and meaningless interpretation of data. In my presentation, I will outline asystems biology solution through the application of parsimony phylogenetic analysis.Maximum parsimony provides a data-based modeling paradigm that will enable a prioristratification of the study cohort(s) in clinical trials, and permits the assessment of earlydiagnosis, prognosis, and treatment efficacy within each stratum.</p>en
dc.description.abstract<p>The advent of omics techniques, such as microarrays and mass spectrometry of metabolomicsand proteomics, and their generated data avalanche has brought in an added level ofcomplexity to biomedical research: mining massively heterogeneous data for biologicalsignificance. Two major issues have become the source of “toxic assets” in biomedicalresearch; the first is the researchers’ underestimation (or lack of awareness) of populationalheterogeneity, and the other is the use of unsuitable analytical bioinformatic paradigms indealing with heterogeneous data. Consequently, we are still struggling with many standingissues such as disease definition and its molecular boundaries, class discovery (i.e., subtypingof disease), early detection, susceptibility to drug side effects, omics biomarkers discovery,specimen profiling (from genetic, proteomic, metabolomic…etc), sorting out of clonal fromnon-expanded mutations, genetic versus epigenetic driving events, post-treatment assessment,and figuring out the primary origin of some cancers.Methods using statistical averaging have their limitations since they are unsuitable for theanalysis of heterogeneity; they hide intrapopulational diversity and homogenizes otherwiseheterogeneous subpopulations. Many researchers are unaware of alternative methods ofanalysis that take into account individual variations, and consequently there is a tremendouswaste of resources and meaningless interpretation of data. In my presentation, I will outline asystems biology solution through the application of parsimony phylogenetic analysis.Maximum parsimony provides a data-based modeling paradigm that will enable a prioristratification of the study cohort(s) in clinical trials, and permits the assessment of earlydiagnosis, prognosis, and treatment efficacy within each stratum.</p>ar
dc.identifier.urihttps://hdl.handle.net/20.500.11888/9350
dc.titleCurrent Issues in Biomedical Researchen
dc.titleCurrent Issues in Biomedical Researchar
dc.typeOther
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