Regrouping of pattern clusters to reveal characteristics of distinct classes and related classes
Bibliographic record
Abstract
Discovering protein patterns for amino acids and their biochemical properties is important for revealing the underlying biophysical models. From this, pattern clustering was introduced in order to relate the discovered protein patterns to taxonomic classes in a localized region of a protein. This paper proposes an algorithm to synthesize and re-group pattern clusters, maximizing their separability in order to reveal class characteristics of the localized region of the protein based on our previous work. To evaluate the pattern clustering and regrouping pattern clusters results, we introduce three evaluation measures: F-measure, class entropy measure, and attribute entropy measure. To validate our proposed algorithm, experiments are run on synthetic data, protein family for amino acid attributes, and chemical property attributes. The experimental results show that: a) the result for regrouping pattern clusters is more accurate in class separation than only using pattern clustering; b) The clusters after regrouping are more distinctly separable with each other than only using pattern clustering; c) two types of pattern clusters are found, with one pertaining to distinct classes and the other associating with two or more related classes; and d) class characteristics are clearly revealed in the data subspace containing the patterns in the pattern clusters. The datasets with chemical properties show that unsupervised techniques can reveal common chemical attributes in the inherent classes as more of the common properties shared by different amino acids are taken into account
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".