Biostatistical Analysis of Microarray Data to Decipher Viral Pathogenesis
Bibliographic record
Abstract
Background: Zika virus, Kunjin virus, Yellow Fever virus, & Sindbis virus belong to Flaviviridae family and are involved in derailing various biological pathways which are not yet elucidated. Aim: Understanding the gene as well as miRNA interplay which plays a vital role in pathogenesis in the diagnosis and prognosis of the disease is of utmost significance. Materials and Methods: By leveraging microarray data from the Gene Expression Omnibus GSE232504 dataset, we meticulously examined the differentially expressed genes & micro RNAs (miRNAs) induced by viral infections. Results: Our analysis revealed 60 statistically significant and differentially expressed genes (DEGs) out of a total of 18,725, with SESN2 (SESTRIN 2) and GADD45A (Growth Arrest and DNA Damage-Inducible Alpha) standing out as highly significant players in the host cell response to these viruses. hsa-miR-148b-3p, hsa-miR-148a-3p, hsa-miR-607 & hsa-miR-5582-3p were the highly expressed micro RNAs (miRNAs). Through functional enrichment analyses, we unveiled significant pathways, including Type 1 Diabetes Mellitus and NF-kappa B Signaling, shedding light on the potential mechanisms underlying these virus-host cell interactions. Furthermore, our PPI (protein-protein interaction) network analysis highlighted key hub genes, while our exploration of miRNA-gene targeting relationships offered valuable insights into post-transcriptional regulation. Conclusion: This study provides a robust foundation for understanding the molecular intricacies of virus-host cell interactions, offering potential targets for further experimental validation and paving the way for innovative therapeutic approaches in combatting viral infections and associated diseases.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".