General-purpose AI gets you started. It won't defend your results.
ChatGPT and Claude Code are useful tools. But scientific analysis demands traceability, reproducibility, and methodological rigor that general-purpose AI was never designed to provide.
No traceability
When you upload a CSV to ChatGPT and ask for a differential expression analysis, you get a script. It may or may not run. There is no record of which package versions were used, no provenance for why one method was chosen over another, and no control over where your data was stored or processed. If a reviewer asks how you reached a finding, you are reconstructing the answer from a chat log.
No reproducibility
Every conversation starts from scratch. The same prompt with the same data can produce different code, different statistical choices, different conclusions. There is no version-locked environment, no provenance chain connecting results to the exact code and dependencies that produced them. Six months later, you cannot rerun the analysis and get the same output.
No methodological depth
General-purpose models run one method and return one answer. They pick a single normalization strategy, a single statistical test, a single set of thresholds, and present the output as if no other reasonable choices existed. They don't cross-validate with alternative approaches, test sensitivity to parameter choices, or flag when a finding depends on a single assumption. The analysis looks complete, but it's shallow.
These tools are fast and flexible. That's exactly the problem. Speed without structure produces findings that don't hold up under scrutiny.
Every result is traceable
Each finding links back to the dataset, the code version, the method, and the parameters that produced it. Audit trails are automatic, not reconstructed after the fact.
Analyses are reproducible by design
Cortex locks the full execution environment: code, dependencies, random seeds, data snapshots. Rerun any analysis and get the same result. Share it with a collaborator and they get the same result too.
Multiple methods, cross-validated
Instead of one pipeline, Cortex runs multiple analytical approaches and compares their outputs. You see where methods agree, where they diverge, and which findings are robust to methodological choices.
Grounded in biological context
Synapse connects every finding to structured evidence from 20+ databases and the scientific literature. Disease associations, druggability scores, pathway context: all scored and cited, not hallucinated.