Bibliographic record
Abstract
Advances in bioinformatics have provided researchers with a large influx of novel sequences, thus making the analysis of the sequences for inherent biological knowledge crucial. Important protein segments can be represented by variable patterns, obtained as set of Aligned Pattern Clusters (APC) by using pattern discovery and pattern synthesis on protein family sequences. We develop a method for clustering APCs based on their co-occurrences on the same protein sequence. Their co-occurrence indicates how protein segments in a protein family interact with one another. The purpose of this paper is to provide a method that, given a list of discovered APCs from a family of a protein sequences, finds a set of interdependent APC clusters with high cooccurrence in sequences of a protein family. The significance of these co-occurrence clusters are verified by their corresponding three-dimensional structure and function of the protein. We applied our method to eight protein families obtained from pFam, including triosephosphate isomerase and ubiquitin. We found that the closely co-occurring clusters of APCs in each protein family are close in the three-dimensional protein structures, inferring interactions of the APC segments. In conclusion, we discover that there is a connection between high co-occurrence between APCs and three-dimensional closeness.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".