Differential Expression Analysis on Schizophrenia Dataset Suggests Pseudogene RNU6-505P as under Selective Pressure
Bibliographic record
Abstract
Schizophrenia is one of the 15 leading causes of disability worldwide. About 1% of the global population has schizophrenia, with 10% of premature mortality chance. Schizophrenia is therefore associated with significant health, social and economic concerns. In this context, thalamus and striatum areas play important roles as much in schizophrenia as processing information before reaching the conscious thought: step happening soon in the creativity action. Creativity is defined by psychological scientists as the generation of ideas or products that are both original and valuable. Creativity relies on imagination and this fundamental human ability remains understudied in comparison to other important psychological phenomena. It is natural to ask whether the gene expression profiling of samples from schizophrenic patients could highlight the activity of some genes specific to humans. Microarray analysis of the dataset GSE25673 revealed that the pseudogene RNU6-505P is expressed differentially in schizophrenic samples and correlates to CYP26A1, ARHGAP18, TSPAN12, HEY2 and TMEM132A genes. Ontological analysis showed that the RNU6-505P pseudogene is involved in brain development and certain neurological pathologies. Evolutionary analysis showed that the AGA 3-nucleotide sequence of RNU6-505P has been under positive selective pressure. Finally, the 1-nucleotide mutation prediction test revealed that variations on the AGA nucleotides could be fatal to the RNA structure of the sequence. We conclude that differential expression of the RNU6-505P pseudogene can be valid to diagnose schizophrenia and the RNU6-505P pseudogene may have a relevant function in the cerebral development and in the divergent evolution of humans from apes.
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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 | 0.001 | 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) | 0.003 | 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".