EigenWorms

Dataset Information
Introduced
2018
License
Unknown
Homepage

Overview

Caenorhabditis elegans is a roundworm commonly used as a model organism in the study of genetics. The movement of these worms is known to be a useful indicator for understanding behavioural genetics. Brown {\em et al.}[1] describe a system for recording the motion of worms on an agar plate and measuring a range of human-defined features[2]. It has been shown that the space of shapes Caenorhabditis elegans adopts on an agar plate can be represented by combinations of six base shapes, or eigenworms. Once the worm outline is extracted, each frame of worm motion can be captured by six scalars representing the amplitudes along each dimension when the shape is projected onto the six eigenworms. Using data collected for the work described in[1], we address the problem of classifying individual worms as wild-type or mutant based on the time series. The data were extracted from the C. elegans behavioural database [3]. We have 259 cases, which we split 131 train and 128 test. We have truncated each series to the shortest usable. Each series has 17984 observations. Each worm is classified as either wild-type (the N2 reference strain) or one of four mutant types: goa-1; unc-1; unc-38 and unc-63. [1] A. Brown, E. Yemini, L. Grundy, T. Jucikas, and W. Schafer, A dictionary of behavioral motifs reveals clusters of genes affecting caenorhabditis elegans locomotion, Proceedings of the National Academy of Sciences of the United States of America (PNAS), vol. 10, no. 2, pp. 791 796, 2013. [2] E. Yemini, T. Jucikas, L. Grundy, A. Brown, and W. Schafer, A database of caenorhabditis elegans behavioral phenotypes, Nature Methods, vol. 10, pp. 877 879, 2013. [3] C. elegans behavioural database

Variants: EigenWorms

Associated Benchmarks

This dataset is used in 1 benchmark:

Recent Benchmark Submissions

Task Model Paper Date
Time Series Classification GRU Parallelizing non-linear sequential models over … 2023-09-21
Time Series Classification TSEM TSEM: Temporally Weighted Spatiotemporal Explainable … 2022-05-25
Time Series Classification LEM Long Expressive Memory for Sequence … 2021-10-10
Time Series Classification coRNN UnICORNN: A recurrent model for … 2021-03-09
Time Series Classification IndRNN UnICORNN: A recurrent model for … 2021-03-09
Time Series Classification UnICORNN UnICORNN: A recurrent model for … 2021-03-09
Time Series Classification expRNN UnICORNN: A recurrent model for … 2021-03-09
Time Series Classification NRDE Neural Rough Differential Equations for … 2020-09-17

Research Papers

Recent papers with results on this dataset: