Decoding Hidden Bio Signals
CovertBiome builds biological intelligence from fragmented biomedical data to uncover hidden signals, map disease mechanisms, and reveal therapeutic relationships.
We are developing a multimodal AI research platform that connects genomics, transcriptomics, proteomics, pathology, clinical evidence, literature, and pathway biology into a structured discovery engine.
Pancreatic ductal adenocarcinoma
A disease context linked to molecular signals, pathways, targets, resistance evidence and therapeutic research hypotheses.
Use the Pancreatic and Kidney buttons to switch programs. The globe is a research relationship map, not a clinical prediction.
Building a computational intelligence layer for disease biology.
Our work focuses on translating fragmented biological evidence into research-ready structure - connecting disease mechanisms, hidden molecular signals, and therapeutic hypotheses.
Disease Mechanisms
Map pathways, genes, phenotypes, tumor states, and molecular drivers associated with disease progression and response.
Drug Relationships
Connect drugs, targets, mechanisms, clinical evidence, and biological context to identify therapeutic relationships and research opportunities.
Hidden Signals
Detect patterns across fragmented datasets that may reveal overlooked structure, patient stratification signals, and novel hypotheses.
From fragmented biomedical data to mechanistic insight.
The CovertBiome platform is designed as a research system combining data harmonization, biological knowledge organization, multimodal machine learning, and representation learning.
CovertBiome public GitHub research repository.
We launched an initial public research repository to share the public-safe scaffolding behind CovertBiome. It introduces our research direction, public proof-of-concept structure, and PDAC-focused starting point without exposing proprietary core reasoning systems.
covertbiome-research
A public research repository focused on multimodal oncology intelligence, beginning with a pancreatic ductal adenocarcinoma (PDAC) proof of concept and public datasets such as TCGA and CPTAC.
github.com/bharati-gaonker/covertbiome-research
What the repository communicates
- Scientific thesis: fragmented biomedical data can be integrated into a useful biological intelligence layer.
- Initial disease focus: PDAC as the first public proof of concept.
- Technical direction: multimodal data harmonization, representation learning, patient-state discovery, and biological signal discovery.
- Execution signal: real public-facing research work and engineering discipline.
Public data first, research integrity always.
Biomedical Data Inputs
- Public omics and disease datasets
- Drug, target, pathway, and mechanism resources
- Biomedical literature and curated annotations
- Clinical trial and therapeutic evidence sources
- Pathology and multimodal evidence as available
Responsible Research Use
- Research platform only - not a clinical diagnostic system
- Designed for hypothesis generation and mechanistic exploration
- Privacy-aware data handling and evidence tracking
- Reproducible workflows and transparent research direction
- Clear separation between public research and proprietary platform IP
Founder-led computational biology research startup.
AI-driven biological intelligence for hidden bio signal discovery, disease mechanism mapping, and therapeutic hypothesis generation.