Prediction of depressive symptoms from socioeconomic data and DNA methylation signatures in depression
Bibliographic record
Abstract
Depression is an increasingly common mental disorder associated with substantial deficits in the quality of life of the patient and increased mortality risk. Several Genomic-Wide Association Studies (GWAS) have been performed to identify genes associated with depression, but its partial heritability among other characteristics suggests the involvement of epigenetic changes in the origin of the disease. Since has been proven a discrepancy between objective and subjective cognition in Major Depressive Disorder (MDD) patients, it is necessary to find a system to detect the disease from a biological perspective. Using a Canadian community-based cohort (n=94) containing DNA methylation data, stratified for early-life socioeconomic status, and assessed for depressive symptoms with the Center for Epidemiologic Studies Depression (CES-D) scale, a differential methylation analysis was performed. From this analysis, 31 cytosine guanine dinucleotides (CpG) were identified as differentially methylated in patients showing depressive symptoms from patients not showing those symptoms. The analysis was performed separating patients by gender and taking the variable age as a covariate. From the socioeconomic and biomolecular variables, and identified CpG sites, a random forest classifier was developed to create a depressive symptoms prediction tool. The resulting algorithm has an accuracy of 73.74% (repeated 15-fold cross-validation, with 3 repeats). The web application Desypre (http://desypre.000webhostapp.com/) was created to allow the public use of the classifier.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".