Biomedical subjects

Annette M Molinaro

Publications and source records attributed to Annette M Molinaro.

3 recordsLinked to original sources

PubMed2022

Evaluation of cross-platform compatibility of a DNA methylation-based glucocorticoid response biomarker.

BACKGROUND: Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450). RESULTS: The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays. CONCLUSIONS: We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care.

Adult↗

PubMed2007

Clinical and pathological findings in a HERDA-affected foal for 1.5 years of life.

A Quarter horse filly bred from two horses affected with HERDA (hereditary equine regional dermal asthenia) was observed clinically and its skin histologically for the 1.5 years of its life. Severe signs of the disease did not manifest until 1.5 years of age, and were not temporally related to saddling. Histological comparison to an age-, breed- and sex-matched control did not show any consistent diagnostic features. Monitoring of the proband substantiated previous reports of (i) the autosomal recessive nature of the disease, (ii) mares affected with HERDA being able to foal without damage to the skin or reproductive tract, (iii) HERDA foals appearing phenotypically normal throughout the first year of life, and (iv) demonstrated that histological interpretation of skin specimens from grossly normal skin may be insufficient to differentiate HERDA-affected horses from controls.

Animals↗

PubMed2005

Prediction error estimation: a comparison of resampling methods.

MOTIVATION: In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to this process: feature selection, model selection and prediction assessment. With a focus on prediction assessment, we compare several methods for estimating the 'true' prediction error of a prediction model in the presence of feature selection. RESULTS: For small studies where features are selected from thousands of candidates, the resubstitution and simple split-sample estimates are seriously biased. In these small samples, leave-one-out cross-validation (LOOCV), 10-fold cross-validation (CV) and the .632+ bootstrap have the smallest bias for diagonal discriminant analysis, nearest neighbor and classification trees. LOOCV and 10-fold CV have the smallest bias for linear discriminant analysis. Additionally, LOOCV, 5- and 10-fold CV, and the .632+ bootstrap have the lowest mean square error. The .632+ bootstrap is quite biased in small sample sizes with strong signal-to-noise ratios. Differences in performance among resampling methods are reduced as the number of specimens available increase. SUPPLEMENTARY INFORMATION: A complete compilation of results and R code for simulations and analyses are available in Molinaro et al. (2005) (http://linus.nci.nih.gov/brb/TechReport.htm).

Algorithms↗