Chaos Game Representations & Deep Learning for Proteome-Wide Protein Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chaos Game Representation (CGR) is an emerging means of visualising and representing genomic and proteomic sequences. There exist many open questions related to its effective application to various computational tasks. In this work, we begin to address some of these questions by comparing four variants of the Chaos Game to generate CGR imagery as part of a multi-class classification task to identify the source organism for a given protein. We propose a novel nodal configuration for icosagon and 20-flake CGRs. Using two datasets, we performed fine-tuning using seven deep convolutional neural network (CNN) architectures and report modest performance over random among the 56 test conditions, highlighting certain shortcomings in effectively leveraging CGR in conjunction with deep CNN architectures. Many of the insights from this work will serve to orient subsequent protein-related studies involving CGR-based encoding and be generally applicable to disparate domains seeking to leverage CGR for sequence-type data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it