Normalized Semi-Covariance Co-Efficiency Analysis of Spike Proteins from SARS-CoV-2 variant Omicron and Other Coronaviruses for their Infectivity and Virulence
Bibliographic record
Abstract
Abstract Spectrum-based Mass-Charge modeling is increasingly used in biological analysis. To explain statistical phenomenon with positive and negative fluctuations of amino acid charges in spike protein sequences from Omicron and other coronaviruses, we propose calculation-based Mass-Charge modeling, a normalized derivation algorithm with exact Excel and MATLAB tool involving separate quadrant extension to normalized covariance, which is still compatible with Pearson covariance co-efficiency. The number of amino acids, molecular weight, isoelectric point, amino acid composition, charged residues, mass-charge ratio, hydropathicity of the proteins were taken into consideration in the analyses, and the relative peak and dip of the average with spike protein sequences based on hydrophobic mass to isoelectric charges of amino acids were also examined. The analyses with the algorithm provide more clear insights leading to revealing underline evolving trends of the viral proteins. Spike proteins from SARS-CoV-2 variants, seasonal and murine coronaviruses were taken as representative examples in this study. The analyses demonstrate that the Mass-Charge covariance co-efficiency can distinguish subtle differences between biological properties of spike proteins and correlate well with viral infectivity and virulence.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".