Deepatom provides a cloud‑based platform that uses pretrained deep‑learning models to automatically analyze high‑dimensional biomedical data such as microscopy images, omics profiles, and spatial transcriptomics. The service ingests raw files, extracts quantitative features, and delivers interactive dashboards and API access for rapid, reproducible insights in research and clinical workflows.
Funding
Funding not disclosed
Founders
Product
Problem
There is a lack of accessible, AI-driven tools that enable researchers and clinicians to efficiently analyze complex molecular and cellular data, leading to slower discovery cycles and suboptimal decision-making in biomedical research.
Solution
Deepatom offers a cloud-based platform that leverages deep learning algorithms to automate the interpretation of high‑dimensional biological datasets such as microscopy images, omics profiles, and spatial transcriptomics. Users upload raw data, and the system applies pretrained neural networks to extract quantitative features, identify patterns, and generate visual reports. The platform integrates with common data storage solutions and provides an API for seamless incorporation into existing analysis pipelines. Results are presented through interactive dashboards that allow users to explore findings, compare cohorts, and export data for downstream validation.
Target Audience
Primary customers are biomedical research laboratories, pharmaceutical R&D teams, and clinical pathology groups that need rapid, reproducible analysis of complex biological data.
Features
- Pretrained deep‑learning models for image segmentation, cell phenotyping, and spatial pattern detection
- Automated pipeline that ingests raw microscopy, histology, and omics files and outputs standardized quantitative metrics
- Interactive web dashboard with customizable visualizations, heatmaps, and statistical summaries
- API and SDK support for integration with laboratory information management systems (LIMS) and bioinformatics workflows
- Scalable cloud infrastructure that handles large datasets with on‑demand compute resources