Discovery of Delta Closed Patterns and Noninduced Patterns from Sequences
Bibliographic record
Abstract
Discovering patterns from sequence data has significant impact in many aspects of science and society, especially in genomics and proteomics. Here we consider multiple strings as input sequence data and substrings as patterns. In the real world, usually a large set of patterns could be discovered yet many of them are redundant, thus degrading the output quality. This paper improves the output quality by removing two types of redundant patterns. First, the notion of delta tolerance closed itemset is employed to remove redundant patterns that are not delta closed. Second, the concept of statistically induced patterns is proposed to capture redundant patterns which seem to be statistically significant yet their significance is induced by their strong significant subpatterns. It is computationally intense to mine these nonredundant patterns (delta closed patterns and noninduced patterns). To efficiently discover these patterns in very large sequence data, two efficient algorithms have been developed through innovative use of suffix tree. Three sets of experiments were conducted to evaluate their performance. They render excellent results when applying to genomics. The experiments confirm that the proposed algorithms are efficient and that they produce a relatively small set of patterns which reveal interesting information in the sequences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".