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
  • Medical Imaging
  • Volumetric Medical Imaging
  • Spatial Omics Data
  • Tokenomics
    • $REX Overview
  • Links
    • rexis.io
  • Term of Use
  • Privacy Policy
  • Community
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  1. Example BioDeSci Data and Models

Overview

Example BioDeSci Data and Models

Our platform provides a robust and secure environment for managing and analyzing diverse biomedical data modalities. It enables:

  • Privacy-preserving data sharing

  • Collaborative model fine-tuning

  • Decentralized model evaluation

These capabilities together foster innovation in biomedical research and healthcare AI.

The sections that follow outline:

  • Common medical data types

  • Their standard formats

  • Foundational or domain-specific model examples

  • How Rexis ensures privacy and security for each modality

From tabular EHRs to high-resolution medical imaging, biological sequences, and spatial omics, Rexis supports a broad range of biomedical data applications within its encrypted AI infrastructure.

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Last updated 1 month ago