Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification.

ABSTRACT: Fungal infections are a global problem imposing considerable disease burden. One of the unmet needs in addressing these infections is rapid, sensitive diagnostics. A promising molecular diagnostic approach is high-resolution melt analysis (HRM). However, there has been little effort in leveraging HRM data for automated, objective identification of fungal species. The purpose of these studies was to assess the utility of distance methods developed for comparison of time series data to classify HRM curves as a means of fungal species identification. Dynamic time warping (DTW), first introduced in the context of speech recognition to identify temporal distortion of similar sounds, is an elastic distance measure that has been successfully applied to a wide range of time series data. Comparison

SUBMITTER: Lu S 

PROVIDER: S-EPMC5338801 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature