Gene-disease associations
The work focuses on separating established biology from emerging and exploratory candidate genes.
Research-use biomedical methods
TensorCortex is a research-use methods project exploring how public biomedical evidence can be structured, scored, and reviewed before disease-gene hypotheses are surfaced.
Methods surface
A restrained workflow for turning source metadata, normalized evidence, and review outcomes into research-priority material.
Research boundary
The project studies how hypotheses can be made more inspectable. It does not provide diagnosis, treatment guidance, patient risk prediction, or clinical decision support.
The work focuses on separating established biology from emerging and exploratory candidate genes.
Candidate claims need visible source context, normalization history, and a path back to disease biology.
Controls, decoys, uncertainty checks, and review gates are treated as part of the research output.
Language can make a hypothesis readable, but it should not invent the hypothesis. The research boundary is evidence first, explanation second.
Source metadata, identifiers, licensing, and provenance stay explicit instead of becoming hidden prompt context.
Evidence rows are normalized before scoring so source-native statistics remain visible and auditable.
Scores rank research candidates; they are not diagnostic, clinical, or causal claims.
Exploratory hypotheses require caveats, failure modes, and suggested validation before public surfacing.
Public pages will only be published after evidence review, uncertainty review, research-use language checks, and source-boundary checks. The current site is here to establish the methods surface without implying medical, diagnostic, or treatment use.