Life Sciences & Healthcare
Genomics & Proteomics
Discover gene regulatory networks and protein interaction dynamics from high-throughput sequencing and mass spectrometry data.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why Genomics & Proteomics teams still struggle to explain what is happening
High-throughput genomics and proteomics produce datasets of extraordinary scale: single-cell RNA-seq, ATAC-seq, mass spectrometry proteomics, and spatial transcriptomics, where regulatory relationships are deeply entangled. Each experiment measures tens of thousands of genes or proteins simultaneously, creating datasets where features vastly outnumber samples. The regulatory networks encoded in this data govern cellular identity, disease progression, and therapeutic response, yet extracting them from raw measurements remains a fundamental challenge in molecular biology.
Correlation-based network inference, dimensionality reduction, and clustering identify statistical associations but cannot distinguish causal regulatory relationships from confounded co-expression. Standard differential expression analysis treats genes independently, missing coordinated regulatory programs. Protein structure analysis adds further complexity: three-dimensional molecular interactions require methods that respect rotational and translational symmetries, which conventional regression and machine learning approaches do not inherently encode.
The CDE Approach
How CDE closes the explanation gap in genomics & proteomics
CDE surfaces gene regulatory dynamics, perturbation responses, and multi-omic structure directly from high-throughput data. Instead of correlation-based inference, it discovers governing regulatory relationships: which transcription factors causally regulate target genes, how epigenetic modifications propagate through regulatory cascades, and how protein-protein interactions form functional networks. For multi-omic integration, CDE jointly analyzes transcriptomic, proteomic, and epigenomic data to discover cross-modal relationships that single-omic analyses cannot detect.
Neural mode handles the rotational and translational symmetries in protein structure data, discovering interaction dynamics that respect molecular geometry. Causal mode distinguishes causal regulation from confounded co-expression through its negative control framework. The Evidence Ledger records complete analytical provenance, supporting the reproducibility standards genomics research demands.
Discovery Engine
How CDE applies here
Neural mode with physics-informed architectures is essential for protein structure and interaction data, where respecting three-dimensional molecular symmetries determines whether discovered dynamics are physically meaningful. Causal mode transforms standard co-expression analysis into directional regulatory network discovery. Neuro-Symbolic mode bridges both domains, encoding complex multi-omic data through neural architectures, with structure extraction yielding interpretable regulatory laws, producing governing equations that researchers can validate through targeted experimental perturbation.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Gene regulatory network causal graphs
- Perturbation response prediction laws
- Protein interaction dynamics
- Epigenetic regulation models
- Multi-omic integration equations
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For genomics & proteomics, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
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Put CDE on a real genomics & proteomics problem
Whether you are exploring genomics & proteomics data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.