A Topological Data Analysis of the Protein Structure
Bibliographic record
Abstract
Persistent homology is a tool from a set of methods called Topological data analysis, showing until nowadays a lot of success when it comes to application in biology since this latest uses metrics only for measuring similarities, Embedding the geometric details and focusing on the global shape is the key point making the success of persistent homology, this will be investigated in the paper since enormous work already done in the field and results seems to be endless, as an efficient topological data analysis tool. In this work we will be confirming the latest assumption (topology embeds geometry) by displaying the structure of COILED SERINE which is a protein estimated to constitute 3-5 percent of the encoded residues in most genomes, and giving a substitute of the optimal characteristic distance that can be used in the flexibility-rigidity index, a classic method used to simulate molecule movements and flexible behavior, when it comes to atomic rigidity functions. We will also analyze interesting patterns in the binding site of the beta sheet generated from the pdb file 2JOX. We will be detecting and giving a simple description of different patterns generated by using javaplex generating barcodes and linear statistical results as a summary statistics.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".