C. elegans, a tiny nematode worm, is used to study a broad range of questions in biology, from diseases to neural function. This apparently simple organism shows a broad repertoire of behaviors incomprehensible to human observer. These behaviors might be representative of its health and disease phenotype.
We employ deep learning methods to quantify and search for distinct motion patterns representative of worm molecular phenotype. The grand challenge of this project involves finding ways to represent worm posture and dynamics. Inspired by methods for image and language processing we aim to find meaningful representations of words and sentences in the language of worm behavior