Analysis of covariations of sequence physicochemical properties
Bibliographic record
Abstract
Abstract:- Sequence analysis often does not take the physicochemical properties into account. On the other hand, some of these properties could be useful in inferring the folding and functional attributes of the molecule when considered with the original sequence information. We evaluated here an analysis using multiple aligned sequences incorporating five physicochemical properties. In addition to site invariance information, we also consider the covariation or interdependence patterns between aligned sites using an information measure. We propose a method based on analyzing the expected mutual information between sites that is statistically significant with a confidence level. When summing the measured information along the aligned sites, we compare the pattern from the measure to the structural and active site of the molecule. In the experiments, the model enzyme molecule lysozyme is chosen. The aligned sequence data are evaluated based on the mapped physicochemical properties of the amino acid residues. Analysis between the original and the transformed sequence data incorporating the physicochemical properties are then compared, subtracted and visualized. From the comparisons, the plots show that some of the selected physicochemical properties in the analysis correlate to the locations of active sites and certain folding structure such as helices. The experiments generally support the useful role of incorporating additional physicochemical properties into sequence analysis, when significance of the statistical variations is taken into account. Key-Words:- protein sequence analysis, physicochemical properties, expected mutual information, statistical significance, lysozyme 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".