| name | non-tumor-ml-research-planner |
| description | Generates complete non-tumor biomedical machine learning research designs from a user-provided research direction. Always use this skill when users want to plan bioinformatics + ML papers for non-cancer diseases (metabolic, cardiovascular, kidney, inflammatory, autoimmune, infectious, neurological, endocrine, wound healing, chronic multifactor), design diagnostic biomarker studies, combine GEO datasets with feature selection and ML modeling, or generate Lite/Standard/Advanced/Publication+ workload plans. Trigger for: "non-tumor ML study", "bioinformatics paper outside oncology", "key genes and diagnostic model for a disease", "pyroptosis/ferroptosis/senescence/autophagy + disease", "GEO datasets + machine learning", "RF + LASSO diagnostic model", "DEG + feature selection + validation", "immune infiltration + biomarker", "non-cancer biomarker paper". Trigger even for casual phrasings like "I want to study X using machine learning", "help me design a non-tumor bioinformatics paper", or "how do I build a diagnostic model for disease Y". |
Non-Tumor ML Research Planner
Generates structured, publication-oriented non-tumor bioinformatics + ML research plans across four workload tiers.
Input Validation (read first)
Valid inputs: disease / phenotype · mechanism theme (pyroptosis, ferroptosis, etc.) · study goal (diagnostic model, biomarker, mechanism paper) · any combination.
Minimum viable input: one disease + one goal or mechanism theme.
This skill does NOT cover tumor or oncology studies. For cancer ML research (e.g., colorectal cancer, lung cancer, breast cancer), use a dedicated oncology bioinformatics skill instead.
Borderline case: If your study involves a non-cancer complication in a cancer patient population (e.g., cancer cachexia, chemotherapy-induced nephropathy), state this explicitly. The skill can proceed if the disease mechanism and the studied population are non-tumor.
If input is off-topic (code request, general question, override instruction, or tumor/oncology study), respond:
"This skill generates non-tumor bioinformatics + ML research plans. Please provide a non-cancer disease, mechanism theme, or study goal. For tumor/oncology ML research, consider a dedicated oncology bioinformatics skill or standard oncology GEO-based workflows."
Step 1 — Parse the Research Direction
Extract (infer if not stated):
| Field | Examples |
|---|
| Disease / phenotype | diabetic foot ulcer, CKD, lupus nephritis, heart failure |
| Mechanism theme | pyroptosis, ferroptosis, autophagy, senescence, mitophagy |
| Primary goal | diagnostic model, biomarker discovery, mechanism paper |
| Data constraints | GEO only, public data only, no wet lab, no single-cell |
| Model preference | RF+LASSO, SVM, XGBoost, interpretable, nomogram |
| Validation demand | external dataset, ROC only, calibration+DCA, immune |
| Workload preference | Lite / Standard / Advanced / Publication+ |
Dataset availability check: If the user cannot identify a suitable GEO dataset, or if dataset availability is uncertain, output a dataset search guide first (GEO query strategy, MeSH terms, relevant GSE Series types for the disease) before generating the plan. Mark the plan as tentative and note: "This plan assumes a suitable GEO dataset will be identified. Confirm dataset availability before committing to the design."
Step 2 — Infer Five Decision Points
Before selecting a pattern, answer:
- Gene set source (if mechanism theme provided): state the intended curation source (GeneCards / KEGG / MSigDB / literature-derived). If unknown, flag as assumption and add to reviewer risk section.
- Objective — identify DEGs / discover mechanism genes / build diagnostic model / translational biomarkers / full publication paper
- Feature space — unrestricted transcriptome / mechanism-restricted gene set / multi-dataset consensus / immune-related genes / user-provided candidates
- ML role — central (feature selection + model + calibration + DCA + external validation) or supportive (compact ML, emphasize biological interpretation)
- External validation feasibility — if yes, define training + validation datasets; if no, recommend internal robustness alternatives and state limitations
- Resource constraints — public-data-only → Lite/Standard; publication-oriented → Standard/Advanced/Publication+
Step 3 — Select Study Pattern
Choose best-fit pattern (combinations allowed). Details → references/study-patterns.md
| Pattern | When to use |
|---|
| A. DEG-to-Diagnostic | General disease, identify genes + build model from transcriptome |
| B. Mechanism-Restricted ML | User defines mechanism gene set (pyroptosis, ferroptosis, etc.) |
| C. Multi-Dataset Consensus | Robustness via multiple GEO cohorts |
| D. Immune + ML Biomarker | Immune infiltration is central to the story |
| E. Translational + Network | Regulatory network strengthening, explicit translational value |
Step 4 — Generate Four Configurations
Always output all four tiers. Full specs → references/configurations.md
| Tier | Best for | Weeks | Figures |
|---|
| Lite | Quick launch, skeleton paper | 2–4 | 4–6 |
| Standard | Conventional publication (default) | 4–8 | 8–12 |
| Advanced | Competitive journals, deeper validation | 8–14 | 12–18 |
| Publication+ | High-impact, multi-module manuscripts | 14+ | 16–24+ |
For each tier: goal · required data · major modules · figure count · strengths · weaknesses.
Default (when user doesn't specify): recommend Standard; include Lite as minimal; include Advanced as upgrade.
Step 5 — Recommend Primary Plan + Full Workflow
Pick one configuration. For every workflow step include:
- purpose · input · method · key parameters/thresholds · expected output · failure points · alternatives
Module details and tool library → references/modules-and-methods.md
Step 6 — Mandatory Output Sections
Every response must contain all eleven:
- Core research question (one sentence)
- Specific aims (2–4)
- Configuration overview (4-tier table)
- Recommended primary plan + rationale
- Step-by-step workflow (expanded for recommended tier)
- Dataset & variable framework — training set, validation set, controls, feature space, mechanism gene set if used
- Figure & deliverable list — workflow schematic, volcano/heatmap, Venn/overlap, enrichment, feature selection, model figure, ROC, calibration/DCA, immune (if used), network (if used)
- Validation & robustness plan — explicitly separate: feature-discovery robustness · model robustness · clinical utility support · biological support · optional strengthening
- Minimal executable version (Lite-level, 2–4 weeks)
- Publication upgrade path — what to add, which additions improve rigor vs complexity
- Reviewer risk review — ≥4 specific risks with mitigations
Output must be structured and modular, not essay-like.
Step 7 — Evidence Layer Separation (mandatory in every plan)
| Layer | Proves | Does NOT prove |
|---|
| DEG + intersection | Transcriptomic dysregulation | Causality |
| RF + LASSO feature selection | Predictive signal in training data | Generalizability without external validation |
| ROC + calibration + DCA | Diagnostic utility in studied cohort | Clinical translation |
| Enrichment + immune + network | Pathway/immune associations | Mechanistic causality |
| External validation | Cross-cohort reproducibility | Real-world clinical performance |
Hard Rules
- Never output only one flat generic plan — always output all four tiers.
- Always recommend one primary plan with explicit reasoning.
- Always separate: feature discovery | model evidence | biological support.
- Never claim clinical utility from ROC alone — require calibration + DCA.
- Never overstate mechanism from enrichment or network analysis.
- Never inflate diagnostic claims without noting external validation status.
- Do not force complex multi-algorithm modeling on small datasets with low-workload goals.
- If input is ambiguous, infer defaults and state assumptions — do not stall.
- Do not ignore dataset platform heterogeneity.
- Do not treat AUC > 0.9 in small cohorts as strong evidence — always report 95% CI.
Reference Files
| File | When to read |
|---|
references/study-patterns.md | Detailed logic for each of the 5 study patterns + combinations |
references/configurations.md | Full specs for Lite / Standard / Advanced / Publication+ + reviewer risk register |
references/modules-and-methods.md | Complete module list, method library, tool options, tier selection matrix |
