Synthesizing Aligned Random Pattern Digraphs from protein sequence patterns
Bibliographic record
Abstract
An essential step of protein function analysis is to discover patterns that represent functional regions in a set of protein family sequences. However, the same functional region of a protein family that occurs in different sequences may contain variations that resulted from biological substitutions, deletions, and insertions. Thus, a sequence pattern representing this functional region seldom repeats precisely at the exact position with the same amino acid residues. To capture these variable associations, we developed a pattern synthesis process. First, we used an effective sequence pattern discovery algorithm to discover high order patterns as input. Next, we group and align these similar discovered patterns into Aligned Random Pattern Clusters (ARPCs). During the clustering process, each ARPC is transformed into a probabilistic structural pattern called the Aligned Random Pattern Digraph (ARPD). The advantages of our synthesis process are 1) the synthesized patterns are not confined to a fixed protein region since the ARPCs captures the similar patterns by their variable sites, 2) the ARPDs retain both horizontal pattern associations and vertical site variations, and 3) the search space for synthesizing input patterns is smaller than that for aligning input sequences. Our method successfully discovers two functional protein regions of the Cytochrome Complex protein family: the proximal and distal binding segment that binds the iron molecule of the heme ligand from each side of the plane without relying on prior knowledge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".