Diag-Nose.io develops the RhinoMAP platform to decode airway biology for respiratory diseases. This platform integrates nasal microsampling, proteomics, and machine learning algorithms to analyze biomarker data. The goal is to enable precision medicine by identifying root causes, leading to faster, more effective treatment selection for patients.
Funding
$940K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current respiratory treatments often lack precision, leading to unpredictable outcomes and reliance on trial and error, which can result in patients experiencing persistent symptoms and side effects from suboptimal therapies. Traditional respiratory tests often fail to capture the unique biological factors driving disease in individual patients. This lack of personalized assessment hinders the ability to match patients with the most effective treatments.
Solution
Diag-Nose.io is developing a precision medicine platform, RhinoMAP, that leverages AI-driven proteomics and computational biology to create a comprehensive respiratory biology model. This model analyzes individual biomarker profiles obtained through nasal microsampling to predict disease activity and treatment efficacy for various respiratory conditions. By matching patients with targeted therapies based on their unique biological makeup, RhinoMAP aims to improve patient outcomes, prevent disease progression, and deliver more effective, personalized respiratory care. The platform is designed to decode airway biology, focusing on root causes to ensure treatments are tailored to the individual patient's needs.
Target Audience
The primary target audience includes clinicians specializing in respiratory care, researchers in respiratory biology, and patients suffering from chronic respiratory diseases such as asthma, chronic sinusitis, and COPD.
Features
- AI-powered biomarker analysis to identify the most appropriate drug for each patient.
- Nasal microsampling for convenient and minimally invasive collection of patient samples.
- Proteomics-based analysis to decode airway biology and identify key biomarkers.
- Machine learning algorithms to predict disease activity and treatment efficacy.
- Comprehensive human dataset tailored for precision respiratory medicine.