Exploration of chaos game representation and integrative deep learning approaches for whole-genome sequencing-based grapevine genetic testing
Bibliographic record
Abstract
Motivation: The identification of grapevine species, cultivars, and clones associated with desired traits is an important component of viticulture. True-to-type identification is very challenging for grapevine due to the existence of a large number of cultivars and clones and the historical issues of synonyms and homonyms. DNA-based identification, superior to morphology-based methods, has been used as the current standard true-to-type method for grapevine, but not without shortcomings, such as the limited number of biomarkers and accessibility of services. Results: To overcome some of the limitations of traditional microsatellite-marker-based genetic testing, we explored a whole-genome-sequencing (WGS)-based approach to achieve the best accuracy at an affordable cost. To address the challenges of the extreme high dimensionality of the WGS data, we examined the effectiveness of using chaos game representation (CGR) to represent the genome sequence data and using deep learning for species and cultivar identification. CGR images provide a meaningful way to capture patterns for use with visual analysis, with the best results showing a 99% balanced accuracy in classifying five species, and a 80% balanced accuracy in predicting 41 cultivars. Our preliminary research highlights the potential for CGR and deep learning as a complementary tool for WGS-based species- and cultivar-level classification. Availability and implementation: Our implementation, including the pipeline for data processing and the four predictive models, is available at https://github.com/pliang64/CGR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".