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Insights et publications

Histoires du fondateur, perspectives éditoriales, publications scientifiques, livres blancs et explications en langage clair.

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BioTwin Non-Medical White Paper

Scientific overview of BioTwin's non-medical virtual twin technology, including longitudinal biomarker tracking, personal baseline modeling, and wellness-only interpretation.

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Récits du fondateur et éditoriaux

When Effort Becomes Debt

Effort is not the same thing as capacity. The most dangerous number in performance is not the workout you completed. It is the debt you did not know you created.

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Publications scientifiques

Prépublication

Metabolomic Profiling of Dried Blood Spots for Breast Cancer Detection: A Multi-Classifier Validation Study in 2,734 Participants

Preprint by Anctil, Hauguel, and Noel showing that untargeted metabolomics from a single dried blood spot detects breast cancer across 2,734 participants, with performance that is robust across six classifier families.

Pourquoi cela compte : Demonstrates that BioTwin's same-lab dried blood spot LC-MS protocol, the one validated for individual identification, carries enough signal to detect breast cancer at scale, with batch-aware validation that avoids the optimistically biased estimates common in the field.

Prépublication

Metabolomic Fingerprinting from Dried Blood Spots Enables Individual Identification Across 1,257 Participants at 94% User-Level Accuracy

Preprint by Hauguel, Anctil, and Noel demonstrating that metabolomic profiles from dried blood spots are stable enough to identify individuals across 18,288 samples and 134 analytical batches.

Pourquoi cela compte : Establishes the methodological foundation for personal-baseline interpretation of longitudinal biomarker data, the core mechanic behind BioTwin's virtual twin model. The same-lab DBS LC-MS protocol and the GroupKFold batch-aware validation standard introduced here underpin all of BioTwin's downstream disease classification work.

Prépublication

Multi-Year Longitudinal Inter-Device Agreement Across Four Consumer Wearables: Concordance, Temporal Diagnostics, and Signal Harmonization

Preprint by Hauguel, Noel, and Anctil quantifying how poorly four consumer wearables agree with each other across an N-of-1 dataset spanning more than 2,400 days, and how much a harmonization layer can recover.

Pourquoi cela compte : Wearable signals are a core input to the virtual twin, so knowing exactly where two devices disagree, and by how much, is a prerequisite for combining them. This work shows that identical metric labels do not guarantee comparable measurements and that device-specific recalibration is often needed.

Publié

Human digital twin technology for individual profiling using LC-MS untargeted metabolomics analysis of dried blood spot samples

Peer-reviewed open-access study (Digital Twin, 2024) by Fradin, Noel and colleagues, the foundational BioTwin work showing that untargeted metabolomics from dried blood spots can profile and identify individuals across 277 volunteers.

Pourquoi cela compte : This is BioTwin's foundational, peer-reviewed publication. It first demonstrated that a self-collected dried blood spot carries enough metabolomic signal to profile and identify an individual, the proof of concept that the large-scale 1,257-participant identification preprint and the disease-detection work were later built on.

Livres blancs BioTwin

Documents scientifiques détaillant la validation, la méthodologie de recherche et le développement de la technologie de jumeau virtuel BioTwin.

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BioTwin Medical White Paper

Scientific overview of BioTwin's medical research methodology, validation approach, and clinical development pathway.

  • How BioTwin builds virtual twins from longitudinal biological data
  • How BioTwin validates research models before clinical use
  • How BioTwin collaborates with clinical and research partners
Voir le livre blanc →
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BioTwin Non-Medical White Paper

Scientific overview of BioTwin's non-medical virtual twin technology, including longitudinal biomarker tracking, personal baseline modeling, and wellness-only interpretation.

  • How BioTwin combines biological, behavioral, and wearable data
  • How personal baselines support longitudinal wellness interpretation
  • How the TwinMe experience stays within non-medical, wellness-only positioning
Voir le livre blanc →

Explications en langage clair