Osteoporosis Gene Interactome: A comprehensive in silico analysis
Bibliographic record
Abstract
Today it is a challenge for clinicians and researchers to understand the comprehensive relationship between molecular and physical functioning of genes towards the progression of disease. Although genomic advancements have produced significant data to identify the genes involved in many complex diseases but a loop exists since a single gene is not attributable to a particular concept. In our work, we tried to find key neighbours involved in osteoporosis by analysing osteoporosis disease module (interactome) using both experimental and clinical methodologies which may also contain mechanisms that are collective with other disease modules. Our idea was strengthened by the findings of previous GWAS p-value studies wherein the level of gene expression was different in both diseased as well as normal conditions. We thus, constructed a gene-gene and protein-protein interaction network for 104 genes linked with 173 genetic variants (single nucleotide polymorphisms) that revealed significant hub proteins which might be fundamentally linked to disease pathogenesis. We further performed gene ontology and functional enrichment analysis followed by KEGG pathway analysis to analyze and validate the role of these genes for their pathophysiological and functional activities. Our analysis revealed the polymorphism in SOST and LRP5 genes as significant conservative SNPs which might have a substantial role in the onset of osteoporosis and its development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".