Structural Classification of Proteins Using Image Based Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
<p>Classification of proteins is an important area of research that enables better grouping of proteins either by their function, evolutionary similarities or in their structural makeup. Structural classification is the area of research that this thesis focuses on. We use visualizations of proteins to build a machine learning class prediction model, that successfully classifies proteins using the Structural Classification of Proteins (SCOP) framework. SCOP is a well-researched classification with many approaches using a representation of a proteins secondary structure in a linear chain of structures. This thesis uses a novel approach of rendering a three dimensional visualization of the protein itself and then applying image based machine learning to determine a protein’s SCOP classification. The resulting convolutional neural network (CNN) method has achieved average accuracies in the range 78-87% on the 25PDB dataset, which is better than or equal to the existing methods.</p>
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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