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EigenWorms
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
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Time Series Classification | EigenWorms | LEM % Test Accuracy 92.3 | Long Expressive Memory for Sequence Modeling | tk-rusch/lem | 8 | Compare |
Papers archive 2025-07-28
5 shown of 5 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 21. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Parallelizing non-linear sequential models over the sequence length | 2 | 1 | 21 Sep 2023 | ran 1 of 1 samples (0 unverified) |
| TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series | 1 | 1 | 25 May 2022 | not harvested |
| Long Expressive Memory for Sequence Modeling | 1 | 1 | 10 Oct 2021 | ran 1 of 10 samples (9 unverified; 1 pointer-only for licence) |
| UnICORNN: A recurrent model for learning very long time dependencies | 1 | 4 | 9 Mar 2021 | ran 1 of 1 samples (0 unverified; 1 pointer-only for licence) |
| Neural Rough Differential Equations for Long Time Series | 4 | 1 | 17 Sep 2020 | ran 5 of 6 samples (1 unverified; 6 pointer-only for licence) |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
No modality tagged.
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- EigenWorms
1 variant name, as the archive lists them.
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