Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning.

ABSTRACT: We present Peax, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned represe

SUBMITTER: Lekschas F 

PROVIDER: S-EPMC8323802 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature