Rexis
  • Rexis: The L1 for DeSci
  • Key Challenges in BioDeSci
    • Overview
  • Data Level: Secure Biomedical Sharing
  • Model Level: Privacy-Preserving Fine-Tuning
  • Evaluation Level: Reproducible Computation
  • The Rexis Solution: Layer for DeSci
    • Overview
    • Data Level: Decentralized Biomedical Data Market
    • Model Level: Privacy-Preserving Training & Inference via Equivariant Encryption
    • Evaluation Level: Secure and Verifiable Biomedical Computation
  • Example BioDeSci Data and Models
    • Overview
  • Tabular Data
  • Biomedical Signals
  • Biological Sequences
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  • Volumetric Medical Imaging
  • Spatial Omics Data
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  1. Key Challenges in BioDeSci

Overview

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Last updated 1 month ago

Biomedical research today frequently involves ad-hoc data-sharing practices that create significant security, privacy, and reproducibility concerns.

To address these issues, Biomedical Decentralized Science (BioDeSci) proposes leveraging blockchain and decentralized technologies for enhanced collaboration, transparency, and efficiency in scientific discovery. However, effectively adopting these technologies introduces distinct challenges at three interlinked levels: the data, model, and evaluation levels.

Each level faces specific privacy, compliance, and operational barriers, motivating the need for advanced privacy-preserving and decentralized solutions.

Status Quo: Fragmented and Insecure Biomedical Data Sharing. Without a secure framework, biomedical institutions often share data manually via email, FTP, or cloud storage services. This results in privacy risks (e.g., exporting raw CSV/JSON/DICOM), lack of auditability and version control, and redundant local data copies. Independent ML pipelines operate in isolation, with no synchronization, verification, or provenance tracking, leading to inconsistency and regulatory overhead