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non-tumor-mechanism-guided-diagnostic-ml-research-planner

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Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

When to use

• disease-vs-control transcriptome comparison
• mechanism-gene restriction
• feature shrinkage
• diagnostic model construction
• ROC / calibration / DCA evaluation
• immune / regulatory interpretation
• orthogonal validation
• multi-dataset merging and batch correction

When not to use

• clinical treatment recommendations
• patient-specific diagnosis
• prescribing
• pure imaging-only AI with no molecular biomarker layer
• pure oncology studies with tumor-specific survival-model logic
• pure wet-lab mechanistic studies with no bioinformatics integration
• non-biomedical / off-topic requests

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