Discovering Patterns From Sequences Using Pattern-Directed Aligned Pattern Clustering
Bibliographic record
Abstract
Functional region identification is of fundamental importance for protein sequences analysis. Such knowledge provides better scientific understanding and could assist drug discovery. Up-to-date, domain annotation is one approach, but it needs to leverage existing databases. For de novo discovery, motif discovery locates and aligns locally homologous sub-sequences to obtain a position-weight matrix (PWM), which is a fixed-length representation model, whereas protein functional region size varies. It thus requires computational expensive exhaustive search to obtain a PWM with width of optimal range. This paper presents a new method known as pattern-directed aligned pattern clustering (PD-APCn) to discover and align patterns in conserved protein functional regions. It adopts aligned pattern cluster (APC) with patterns of variable length and strong support to direct the incremental APC expansion. It allows substitution and frame-shift mutations until a robust termination condition is reached. The concept of breakpoint gap is introduced to identify spots of mutations, such as substitution and frame shifts. Experiments on synthetic data sets with different sizes and noise levels showed that PD-APCn outperforms MEME with much higher recall and Fmeasure and computational speed 665 times faster that MEME. When applying to Cytochrome C and Ubiquitin families, it found all key binding sites within the APCs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".