Srinivas V Koduru PhD AI/ML master research brain — 15 skills covering cancer biomarker ML pipelines, R/Bioconductor, advanced biostatistics, literature intelligence, lab automation, publication figures, clinical study design, scientific writing with humanizer, FDA regulatory submissions, patent/IP strategy…
Use when building ML models for cancer diagnostics, biomarker analysis, qPCR data, OpenArray, small RNA/mRNA panels, survival analysis, gene expression, liquid biopsy, or any clinical prediction task.
Use when designing clinical or diagnostic studies — sample size calculation, power analysis, study type selection (case-control, cohort, diagnostic accuracy), CONSORT/STROBE/STARD checklists, IRB protocol structure, or biospecimen collection SOPs for cancer biology and diagnostics research.
Use when creating publication-quality figures for scientific journals — volcano plots, heatmaps, ROC curves, Kaplan-Meier, forest plots, UMAP, box/violin plots, or multi-panel figures in R (ggplot2) or Python (matplotlib/seaborn) with journal submission specs (300 DPI, TIFF/EPS, color-blind safe).
Use when starting any session for Dr. Koduru — loads expertise context as Senior Research Scientist in Cancer Biology, Bioinformatics, and AI/ML diagnostics with active projects in UroDETECT, qPCR, UTI detection, and colorectal cancer screening.
Use when writing lab automation scripts — OpenTrons protocols, liquid handler programming, LIMS integration, 96/384-well plate layouts, barcode/sample tracking, pipetting worklists, or automating any wet lab workflow with Python for qPCR setup, serial dilutions, sample aliquoting, or plate reformatting.
Use when performing literature searches, systematic reviews, citation analysis, research gap identification, PubMed queries, building evidence tables, PRISMA flow diagrams, or preparing background sections for grants and manuscripts in biomedical research.
Use when drafting provisional patent applications for diagnostic assays, biomarker panels, or medical devices — claims structure, patent vs trade secret decisions, freedom-to-operate analysis, IP landscape assessment for grants (SBIR commercialization), or protecting intellectual property in translational cancer…
Use when starting any new project, web app, ML model, analysis, or diagnostic tool — creates PROJECTBRIEF.md in project root and adds entry to PROJECTSREGISTRY.md.
Use when performing bioinformatics analysis in R — differential expression (DESeq2, limma, edgeR), single-cell RNA-seq (Seurat, Harmony), pathway enrichment (clusterProfiler, GSEA, fgsea), survival analysis (survival, survminer), or any Bioconductor workflow for gene expression, small RNA, or mRNA data.
Use when preparing FDA submissions (510(k), De Novo), CLIA validation reports, CAP compliance documents, or making regulatory pathway decisions for diagnostic assays including LDT vs IVD.
Use when writing scientific manuscripts, grant applications (NIH R01/R21, SBIR/STTR, NSF, DoD CDMRP), conference abstracts (ASCO, AACR, CAP, AMP), figure legends, or statistical reporting for biomedical and diagnostics papers — enforces expert academic voice and AI detection avoidance.
Use when building any web application, API, scientific dashboard, reporting service, or mobile app — enforces security-first patterns including auth, RBAC, input validation, and HIPAA/CLIA-aware data handling.
Use at end of any session where new ML patterns, assay fixes, code solutions, or clinical discoveries were made — captures learnings to KNOWLEDGEBASE.md.