Classifying Proteins by Amino Acid Variations of Sequential Patterns
Bibliographic record
Abstract
Similarities and differences in protein sequence patterns can be used to reveal essential and class-specific functionality of protein families. Traditional supervised learning methods require class labels for classifying sequences but cannot reveal embedded patterns related to inherent functionality and taxonomical variations. We develop algorithm for discovering statistically significant sequence patterns and then aligning and clustering them into Aligned Patterns Clusters (APCs). We measure APC's classification ability: 1) with semi-supervised information measures that require class labels such as: a) class entropy (H) for patterns and each amino acid on a column; and b) class information gain (IG) for each column based its class amino acid distribution and 2) unsupervised measure without relying on class labels, such as: a) Entropy Redundancy (R1) that reflects amino acid conservation and diversity acid in a column and b) Normalized Sum of Mutual Information Redundancy (SR2) which characterizes the dependence of a column with all the other columns in the APC. We applied our Aligned Pattern Synthesis Process on: a) spermidine / spermine-N1-acetyltransferase (SSAT), b) the cytochrome c, and c) the ubiquitin protein families. After validating the classification ability of each of the proposed measures through a simple synthetic data set and the SAAT data, we present results on the other two protein families in a selective manner. In all our experiments, we have demonstrated the ability of each proposed measure and confirm the correlation between the SR2 with R1 and IG. Our experiments reveal how sequence patterns of the rows and amino acid distribution on each column can be associated with class and will be useful for amino acid substitution study, thus avoiding the dependencies on class label, which are often unavailable, inaccurate, or unbalanced. Properties of the measures, computational efficiency and biological impact of the algorithms are discussed in the paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".