3D Deformable Protein Shapes Classification based on Triangles-Stars and Composite Deep Neural Networks
Bibliographic record
Abstract
In this paper, the problem of 3D protein deformable shape classification is addressed. Proteins are macromolecules with deformable shapes, their classification based only on their molecular surfaces is challenging problem. In addition, the protein shapes are related to their functions which makes their classification an important task. Triangular meshes (graphs) my be considered to represent the protein molecular surfaces. In this paper, we propose a new deep learning-based approach for 3D protein deformable shape classification. We propose a 3D deformable shape descriptor and a composite deep neural network. The shape descriptor is based on a set of global and local features resulting from the decomposition of protein 3D shapes into triangles-stars, following the different neighborhood order considered. The classification is performed by a composite deep neural network, each branch corresponding to a neighborhood order. The proposed approach is evaluated against 3D protein benchmark repositories and state-of-the-art methods. Our experimental results show and demonstrate the high performances and the effectiveness of our approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".