Protein structural and sequence analysis of human ACE2 using prediction and modeling bioinformatics tools for diagnostics biomarkers and drug design features: an opinion study path
Bibliographic record
Abstract
Angiotensin converting enzyme-2 receptor (ACE2) present on human cell membrane surfaces is a critical receptor for binding of SARS-CoV-2 to invade cells. Severe burden of recent COVID-19 pandemic was of global public health importance. Using bioinformatics tools, protein sequence and structural features of ACE2 protein can be obtained. Protein sequences can be compared with structures of ACE2 proteins from same individuals (or patients) of different clinical status. This can either be from data obtained from disease states or already deposited annotated data. Then assess for effectiveness of a pool of bio-molecular attributes such structural neighbor profiles which help describe micro-environment of single amino polymorphisms (SAPs). Then engage predictive and modeling tools to predict key mutational loci- particularly involving SAPs and their functions in relation to disease states. Generated data can contribute to ontology, screens to identify biomarkers and open up paths to development of research protocols in therapeutics and diagnostics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".