Mfx

Mfx provides a cloud‑native platform that unifies data ingestion, preprocessing, and advanced analytics for genomics, proteomics, and imaging. The system automates quality control, normalization, and feature extraction, then applies built‑in or customizable machine‑learning models to deliver biomarker discovery, phenotype classification, and predictive insights via interactive dashboards and API/FHIR exports. It offers scalable compute, reproducible workflow management, and secure integration with LIMS and EHR systems for biomedical researchers and clinical labs.

Traction 10/10

Stevenage, United Kingdom · HQ

Founded 2020435K+ followers

Artificial Intelligence
Data & Analytics
Biotechnology
Healthcare Technology

Updated 5 months ago

Funding

Raised to date

$4MRaised 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.

Funding rounds are not available yet.

Founders

1 founder

Césaré Cejas

Co-founder

Product

Problem

Researchers and clinicians need reliable, high‑throughput methods to analyze complex biological data (e.g., genomics, proteomics, imaging) but existing tools are often fragmented, require extensive manual preprocessing, and lack integrated analytics, slowing discovery and decision‑making.

Solution

Mfx offers a cloud‑native platform that consolidates data ingestion, preprocessing, and advanced analytics into a single workflow. Users upload raw datasets, and the system automatically applies standardized pipelines—including quality control, normalization, and feature extraction—using scalable compute resources. Built‑in machine‑learning models generate actionable insights such as biomarker identification, phenotype classification, and predictive outcomes. Results are presented through interactive visualizations and can be exported via APIs for downstream integration with laboratory information systems or electronic health records. The platform’s modular architecture allows customization of pipelines to fit specific research protocols while maintaining reproducibility and compliance with data‑privacy regulations.

Target Audience

Primary users are biomedical researchers, pharmaceutical R&D teams, and clinical laboratories that require integrated data analysis pipelines for high‑throughput omics and imaging studies.

Features

Automated end‑to‑end data processing pipelines for genomics, proteomics, and imaging modalities
Scalable cloud compute with on‑demand resource allocation to handle large‑scale datasets
Pre‑trained and customizable machine‑learning models for biomarker discovery and predictive analytics
Interactive dashboards with real‑time visualizations, cohort comparisons, and statistical reporting
Secure API and FHIR‑compatible export for seamless integration with LIMS, EHR, and third‑party tools
Version‑controlled workflow management ensuring reproducibility and auditability