Browsing by Subject "AUC"

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    AUCTSP: an improved biomarker gene pair class predictor
    (BMC, 2018-06-26) Kagaris, Dimitri; Khamesipour, Alireza; Yiannoutsos, Constantin T.; Biostatistics, School of Public Health
    The Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions ("pivot" genes). RESULTS: We introduce the AUCTSP classifier as an alternative rank-based estimator of the magnitude of the ranking reversals involved in the original TSP. The proposed estimator is based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and as such, takes into account the separation of the entire distribution of gene expression levels in gene pairs under the conditions considered, as opposed to comparing gene rankings within individual subjects as in the original TSP formulation. Through extensive simulations and case studies involving classification in ovarian, leukemia, colon, breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative (pivot) genes. CONCLUSIONS: The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across all subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair.
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    Elucidating the mechanisms or interactions involved in differing hair color follicles
    (2016) Muralidharan, Charanya; Walsh, Susan
    Forensic DNA phenotyping is an up and coming area in forensic DNA analyses that enables the prediction of physical appearance of an individual from DNA left at a crime scene. At present, there has been substantial work performed in understanding what genes/markers are required to produce a reliable prediction of categorical eye and hair color from the DNA of an individual of interest. These pigmentation markers (variants from HERC2, OCA2, TYR, SLC24A4, SLC45A2, IRF4 to name a few) are at the core of several prediction systems for eye and hair color such as IrisPlex, HIrisPlex, and the Snipper 2.5 suite. The contribution of these markers towards prediction in most cases however, only factors in an independent effect and do not take into account potential interactions or epistasis in the production of the final phenotypic color. Epistasis is a phenomenon that occurs when a gene’s effect relies on the presence of ‘modifier genes’, and can display different effects (enhance/repress a particular color) in genotype combinations rather than individually. In an effort to detect such epistatic interactions and their influence on hair color prediction models, for this current study, 872 individuals were genotyped at 61 associative and predictive pigmentation markers from several diverse population subsets. Individuals were phenotypically evaluated for eye and hair color by three separate independent assessments. Several analyses were performed using statistical approaches such as multifactor dimensionality reduction (MDR) for example, in an effort to detect if there are any SNP- SNP epistatic interactions present that could potentially enhance eye and hair color prediction model performances. The ultimate goal of this study was to assess what SNP-SNP combinations amongst these known pigmentation genes should be included as an additional variable in future prediction models and how much they can potentially enhance overall pigmentation prediction model performance. The second part of the project involved the analyses of several differentially expressed candidate genes between different hair color follicles of the same individual using quantitative Real Time PCR. We looked at 26 different genes identified through a concurrent non-human primate study being performed in the laboratory. The purpose of this study was to gain more insight on the level of differentially expressed mRNA between different hair color follicles within the same human individual. Data generated from this part of the project will act as a pilot study or ‘proof of principle’ on the mRNA expression of several pigmentation associated genes on individual beard hair of varying phenotypic colors. This analysis gives a first glimpse at expression levels that remain constant or differentiate between hairs of the same individual, therefore limiting the contribution of individual variation.