Guy A. Rouleau: Genetic foundations of neurological disease – From risk variants to pathogenic mechanisms
Bibliographic record
Abstract
In this engaging Genomic Press Interview, Dr. Guy A. Rouleau, OC, OQ, FRCPC, FRSC, FAAN, opens up about his remarkable journey from a young boy conducting chemistry experiments in his basement to becoming Director of The Neuro (Montreal Neurological Institute-Hospital) and Chair of McGill University's Department of Neurology and Neurosurgery. A world-renowned clinician and neurogenetics researcher, Dr. Rouleau has dedicated 35 years to uncovering the genetic foundations of devastating neurological conditions, identifying dozens of disease-causing genes in ALS, hereditary neuropathies, epilepsy, schizophrenia, and autism. His scientific impact is reflected in nearly 1,000 peer-reviewed publications cited over 110,000 times, but his vision for transforming research methodology may be his most lasting legacy. As co-founder of the Tanenbaum Open Science Institute and First Vice-President of the World Federation of Neurology, he is pioneering a revolution in scientific collaboration by establishing The Neuro as the world's first academic institution fully committed to open science principles. Through personal anecdotes and professional insights, Dr. Rouleau shares his passion for sailing, commitment to mentoring the next generation, and unwavering belief that open, collaborative science will accelerate discoveries to benefit patients worldwide.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".