Dr. Archer’s primary research area has been in the development of statistical methods and computational algorithms for analyzing high-throughput genomic data. Such technologies yield datasets consisting of a large number of candidate predictors (p, the number of genes or proteins) on a small number of observations (n, the number of samples). Therefore, appropriate methods of adjusting for multiple comparisons in the presence of high collinearity are still the subject methodological research. Her current research is focused on developing methods and software when the response is either ordinal or discrete and the genomic data are collected cross-sectionally or longitudinally.
Statistical methods for the analysis of data from high-throughput genomic assays, Discrete and ordinal response modeling, Statistical computing, Supervised learning and data mining
Archer KJ, Dumur CI, Joel SE, Ramakrishnan V. Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixed effects models. Biostatistics, 7(2):198-212, 2006.
Archer KJ and Kimes RV. Empirical characterization of random forest variable importance estimates. Computational Statistics and Data Analysis, 52(4): 2249-2260, 2008.
Archer KJ, Mas VR. Ordinal response prediction using bootstrap aggregation, with application to a high-throughput methylation dataset. Statistics in Medicine, Dec 20;28(29):3597-610, 2009.
Archer KJ, Reese SE. Detection Call Algorithms for High-throughput Gene Expression Microarray Data. Briefings in Bioinformatics, 11(2):244-52, 2010.
Archer KJ, Zhao Z, Guennel T, Maluf DG, Fisher RA, Mas VR. Identifying genes progressively silenced in preneoplastic and neoplastic liver tissues. International Journal of Computational Biology and Drug Design, 3(1), 52-67, 2010.
Asomaning N, Archer KJ. High-throughput DNA methylation datasets for evaluating false discovery rate methodologies. Computational Statistics and Data Analysis, 56(6):1748-1756, 2012.
Archer KJ, Williams AAA. L1 penalized continuation ratio models for ordinal response prediction using high-dimensional datasets. Statistics in Medicine, 31(14):1464-74, 2012.
Archer KJ, Hou J, Zhou Q, Ferber K, Layne JG, Gentry AE. ordinalgmifs: An R package for ordinal regression in high-dimensional data settings. Cancer Informatics, 13:187-95, 2014.
Hou J, Archer KJ. Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data. Statistical Applications in Genetics and Molecular Biology, 14(1):93-111, 2015.
Ferber K, Archer KJ. Modeling discrete survival time using genomic feature data. Cancer Informatics, 14(Suppl 2):37-43, 2015.
Makowski M, Archer KJ. Generalized monotone incremental forward stagewise method for modeling count data: Application predicting micronuclei frequency. Cancer Informatics, 14(Suppl 2):97-105, 2015.
Gentry AE, Jackson-Cook C, Lyon D, Archer KJ. Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces. Cancer Informatics, 14(Suppl 2):201-8, 2015.