Pharmacogenomics and animal models of schizophrenia
Bibliographic record
Abstract
Abstract Schizophrenia is a syndromal brain disease of largely unknown pathophysiology and most likely heterogeneous etiology in which genetic predisposition constitutes the major risk factor. In recent years, a shift from a monolithic view of the disorder is leading to its dissection into component phenotypic modules or endophenotypes that may differ in pathophysiology, underlying genetic diathesis, or treatment response. Reducing phenotypic heterogeneity by focusing on endophenotypes will facilitate the production of valid animal models to be used in experimental approaches, improve our chances of uncovering genes predisposing to the disease in linkage or association approaches, and simplify generation of novel molecular targets for the drug discovery process. We hereby review some recently generated mouse models that replicate specific endophenotypes observed in schizophrenia and that implicate putative contributing genes that may be exploited to explore novel drug targets. These are derived from opposing but complementary perspectives. One approach developed in our work begins with mouse models of schizophrenia traits to uncover candidate schizophrenia genes. Another approach followed by several other groups begins with putative schizophrenia vulnerability genes to investigate the corresponding endophenotype in mouse models. Combined with global analysis of gene expression, these mouse models offer the hope that the disease‐causing and treatment pathways implicated in schizophrenia will finally be unraveled. Drug Dev. Res. 60:95–103, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".