Duplex Bioscience develops a proprietary platform, microBIS.io, that utilizes artificial intelligence and machine learning for the identification of bacteria and diagnosis of antimicrobial resistance through wastewater analysis. By creating Africa's largest multi-omics database, the company enhances precision diagnostics for communicable and non-communicable diseases in low-resource healthcare settings.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional methods for bacterial identification and antimicrobial resistance (AMR) diagnosis are often time-consuming and resource-intensive, particularly in low-resource healthcare settings. The lack of comprehensive data on communicable and non-communicable diseases hinders the development of effective diagnostic and treatment strategies.
Solution
Duplex Bioscience has developed microBIS.io, a proprietary platform that leverages artificial intelligence (AI) and machine learning (ML) to identify bacteria and diagnose antimicrobial resistance through wastewater analysis. The platform digitizes and analyzes AMR data, providing insights for researchers, diagnostic scientists, and hospitals. By building a large multi-omics database focused on African populations, the company aims to improve precision diagnostics and develop context-specific diagnostic tools. The platform facilitates diagnostic biomarker discovery and the development of molecular diagnostic point-of-care solutions.
Target Audience
The primary target audience includes researchers, diagnostic scientists, and hospitals in Africa focused on infectious disease research, discovery, and management.
Features
- AI-powered analysis of wastewater samples for bacterial identification and AMR detection
- Africa's largest multi-omics database for improved diagnostic biomarker discovery
- Next-gen high-throughput methods for screening communicable and non-communicable diseases
- Molecular diagnostics point-of-care development and R&D
- Digitization of AMR data for researchers, diagnostic scientists, and hospitals