Jeremie Poschmann: Data-driven discovery in human diseases through multi-omics profiling of the circulating immune system
Bibliographic record
Abstract
Dr. Jeremie Poschmann leads a research group at INSERM and Université de Nantes, where he investigates the human immune system with a focus on the circulating immune compartment. His work combines multi-omics and data-driven approaches to uncover immune mechanisms that influence disease susceptibility and patient outcomes, particularly in infectious and psychiatric conditions. Trained originally as a nurse, Dr. Poschmann entered science driven by a deep curiosity for the unresolved complexities of human biology. His career has taken him through Germany, Belgium, Canada, Singapore, the UK, and France, shaping his collaborative and cross-disciplinary mindset. A self-taught bioinformatician, he values independence in research and actively fosters a diverse and inclusive team. In this Genomic Press Interview, he reflects on pivotal moments in his journey including an early fascination with genome-wide discovery and shares how pre-existing immune states may help explain why individuals respond differently to disease exposure. Outside the lab, he finds balance through surfing and chess, which keeps his thinking sharp. Committed to translating research into real-world impact, Dr. Poschmann is equally passionate about mentoring emerging scientists and building a culture that supports innovation and integrity.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".