title: From small studies to precision medicine: prioritizing candidate biomarkers.
abstract: There are still many open questions in data-analytic research pertaining to biomarker development in the era of personalized/precision medicine and big data. Among them is the question of what constitutes best practice for the extraction of prioritized lists of candidate biomarkers from smaller studies that are 'hypothesis generating' in nature. A recent comparison of methods to detect patient-specific aberrant expression events in small- to medium-sized (10 to 50 samples) studies provides results that favor the use of outlying degree methods. See related Research, http://genomemedicine.com/content/5/11/103.
authors.affiliation: Department of Biostatistics, SUNY University of Buffalo, 433 Kimball Tower, 3435 Main Street, Buffalo, NY 14214, USA.
authors.forename: Daniel P
authors.initials: DP
authors.lastname: Gaile
authors.affiliation: Department of Biostatistics, SUNY University of Buffalo, 433 Kimball Tower, 3435 Main Street, Buffalo, NY 14214, USA.
authors.forename: Jeffrey C
authors.initials: JC
authors.lastname: Miecznikowski