An integrated genomic analysis on miRNAs and SNPs associated with vitiligo to reveal potential drug candidates
Bibliographic record
Abstract
Vitiligo is characterized by the occurrence of depigmented patches on the skin caused as a result of progressive loss of functional melanocytes. It is a polygenic disorder entailing both genetic and non-genetic factors intricately and as such is not yet clearly understood. Hence, a comprehensive understanding of the entire spectrum of disease susceptibility and pathogenesis remains a challenge. Emerging evidence over the decades underlines the cardinal role of miRNAs in disease development and having a significant role in melanocyte development and survival. Also, identifying the susceptible genes and their variants that influence disease onset is fundamental to unravel the rationale for disease susceptibility. We, therefore, applied a systems biology approach to identify potential miRNAs and their target genes to construct a miRNA-target gene network revealing essential miRNAs that might be significantly related to vitiligo. We further investigated the susceptible genetic variants associated with vitiligo and prioritized a few proteins in our protein-protein interaction network as significant hub proteins. Network-based polypharmacological studies of such hub proteins are helpful in analyzing a large set of disease-associated proteins which might initiate better diagnosis with the feasibility of personalized treatment for vitiligo patients in the future. Our polypharmacogenomic systems analysis highlighted novel drug candidates and drug repositioning candidates for vitiligo. Furthermore, we investigated the pathogenic effect of the plausible single nucleotide polymorphisms (SNPs) using computational platforms and carried out preliminary protein modeling to implicate the role of SNPs in disease pathogenesis. Thus, our analysis unveiled significant findings which may provide an insight of the mechanisms of vitiligo development and progression, thereby, driving the way towards improved therapeutic and diagnostic interventions for vitiligo management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".