150. PROGRESS OF TRANSCRIPTOMIC AND EPIGENOMES STUDY IN THE DEPRESSION AND SUICIDE STUDY
Bibliographic record
Abstract
Abstract Background Major depressive disorder (MDD) is a common but serious mental illness and one of the mental illnesses associated with suicide. Both MDD and suicide are complex diseases affected by genetics, environment and their interactions. From the omics level, exploring the etiology of complex diseases plays an important role in discovering new therapeutic targets and identifying potential biomarkers for high-risk individuals. The rapid development of high-throughput sequencing technology, especially the emergence of single-cell sequencing, spatial transcriptome sequencing and proteomics technology in recent years, has been applied to the study of the biological mechanisms of depression and suicide. Using new omics technologies, researchers have generated a large amount of data at different omics levels from postmortem brain tissue, patient blood and animal models. These omics data have shown changes of the molecules at the genome-wide level and also reflect the biological state of the disease. Artificial intelligence technology is another hot topic in the current research. More and more studies integrate advanced bioinformatics analysis and artificial intelligence methods to study the development of MDD and the mechanism of suicide. Aims & Objectives To discover potential molecular targets and effective biomarkers to prevent suicide Method We focused on several hot issues in the field of depression and suicide, including depression and psychological resilience, gender differences, early life stress, comorbidity of depression and suicidal behavior, and conducted a combination of dry and wet research studies to understand the development of mood diseases. Results We will present the following research results: 1) Establishing a new integrated method including transcriptome data analysis and machine learning; 2) Identifying phenotype-specific or overlapping molecules, modules, and biological pathways related to depression, resilience, early life stress, sex differences, etc.; 3) Studying the gene expression patterns of epigenetic regulation in human depression brain tissue Discussion & Conclusions By establishing novel integrated analysis, we reported some novel molecular targets and biomarkers. On the basis of those study, we will further explore the spatial expression patterns and the rules of epigenetic regulation based on chromosomes of depression and suicide brain tissue.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".