K R&D Lab
│
├── 🧪 SCIENCE            — biology, medicine, plant science, ecology, chemistry, cognition
├── 🚀 ENTREPRENEURSHIP   — ventures, public cases, ecosystem signals, applied investigations
└── 💻 TECHNOLOGY         — ML tools, bioinformatics pipelines, reproducible methods, infrastructure

How findings are meant to be used:

• Scientists → take hypotheses into wet-lab or field validation
• Founders / operators → evaluate opportunities, systems, and decision logic
• Students & researchers → replicate, extend, cite
• Developers → reuse tools, pipelines, dashboards, and open infrastructure

Computational approaches to natural sciences. Methods: bioinformatics, cheminformatics, statistical modeling, network analysis.

Computational models for cancer biology, RNA therapeutics, nanoparticle delivery, biomarkers, and rare cancers.

S1 — Biomedical & Oncology
│
├── 🧬 S1-A · PHYLO-GENOMICS    ← Genomics & Variants
├── 🔬 S1-B · PHYLO-RNA         ← RNA Therapeutics
├── 💊 S1-C · PHYLO-DRUG        ← Drug Discovery
├── 🧪 S1-D · PHYLO-LNP         ← Nanoparticle Delivery
├── 🩸 S1-E · PHYLO-BIOMARKERS  ← Biomarkers & Diagnostics
└── 🧠 S1-F · PHYLO-RARE        ← Rare Cancers / Frontier

What bioactive compounds do plants produce — and what can they do?

How do soil microbiomes support plant growth — and how can we engineer them?

What metabolic signatures distinguish healthy from diseased states?

What computational patterns predict neurodegeneration and aging?

Environmental microbiomes, biodiversity, and climate-linked computational ecology.

A science-facing lane for measurable life systems, cognition, adaptive training, self-tracking, and longitudinal human-pattern research. It now uses an A–L life-sphere structure so each major life domain can become measurable when needed.

Where master prep belongs:

• Primary home: 📚 S7 — K Life OS
• Scientific sub-lane: S7-I · 🔎 Career or Education
• Current project: R1 - Master Prep Analytics

This way it is treated first and fully as a learning-and-cognition research line inside the science sphere.

Applied research for decision-making, venture design, operating systems, market intelligence, ecosystem signals, and visible public cases.

Opportunity framing, venture logic, product direction, operating hypotheses, and decision systems that help ideas become structured bets rather than loose intuition.

E1 ? Venture, Product & Opportunity Systems
?
??? E1-R1  Opportunity Mapping & Problem Framing
??? E1-R2  Product / Venture Validation
??? E1-R3  Operating System Design

Audience signals, segmentation, campaign logic, positioning research, and behavioral patterns translated into practical market insight.

E2 ? Market, Audience & Behavioral Intelligence
?
??? E2-R1  Audience Segmentation
??? E2-R2  Campaign & Messaging Effectiveness
??? E2-R3  Consumer Behavior Modeling

Ecosystem mapping, partnership landscapes, social/open signals, and external monitoring that help locate leverage, context, and strategic timing.

E3 ? Ecosystem, Partnerships & External Signals
?
??? E3-R1  Ecosystem Mapping
??? E3-R2  Partnership & Stakeholder Landscapes
??? E3-R3  Open, Social & Signal Tracking

Cross-domain investigations that are visible, systems-facing, and useful as public case studies rather than private notes.

E4 ? Applied Investigations & Public Cases
?
??? E4-A ? Systems & Workflow Cases
??? E4-B ? Learning & Preparation Cases
??? E4-C ? Life OS & Longitudinal Self-Research Cases

How to use E4 correctly:

• E4-A ? workflows, operations, process evolution, system cleanup
• E4-B ? preparation dashboards, learning cases, adaptive progress stories
• E4-C ? broader life-system analytics only when they become real longitudinal research rather than private journaling

Computational tools, automation, reproducibility, dashboards, and open research infrastructure. Methods: machine learning, NLP, statistical modeling, software engineering, and interface design for usable research systems.

Reusable engines, models, and pipelines for scientific and analytical work.

T1 ? Research Tools, ML & Analytical Engines
?
??? T1-R1  OpenVariant Engine
??? T1-R2  Corona ML Pipeline
??? T1-R3  AutoCorona NLP
??? T1-R4  Synthetic Lethal Finder

Frameworks, scoring systems, confidence labels, evaluation logic, and reproducible analytical methodology.

T2 ? Reproducibility, Scoring & Method Systems
?
??? T2-R1  Research Gap Scoring
??? T2-R2  Confidence Labeling
??? T2-R3  Reproducible Evaluation Workflows

Reusable interfaces, public dashboards, literature-gap tooling, registries, and open infrastructure that make research more usable and inspectable.

T3 ? Dashboards, Interfaces & Open Infrastructure
?
??? T3-R1  Dashboard Templates & Public Interfaces
??? T3-R2  Literature Gap Detection
??? T3-R3  Dataset Registries & Open Research Infrastructure

Naming pattern: SPHERE-DIRECTION_RN_MonthYear

Examples:
  S1-Biomedical_R1_03-2026       ← OpenVariant
  S1-Biomedical_R11_06-2026      ← LNP in CSF
  S2-Plant_R1_09-2026            ← Phytochemical profiler
  S7-CareerEducation_R1_03-2026  ← master prep analytics / preparation research
  E4B-LearningCases_R1_03-2026   ← public dashboard mirror for preparation case
  T1-MLTools_R2_04-2026          ← reusable research pipeline

Standard repo structure:

• README.md — research question, methods, key findings
• report.md — full findings plus confidence labels
• CITATION.cff — citation metadata
• LICENSE — MIT
• requirements.txt — Python dependencies
• app.py — Gradio interactive demo if applicable
• data/raw/ — original public datasets or download scripts
• data/processed/ — cleaned, analysis-ready data
• figures/ — plots and visualizations
• execution_trace.ipynb — reproducible notebook

New to the lab?

• Start with the demo spaces and readable repo overviews
• Move from beginner review to reproducible notebooks and reports

Scientist / researcher?

• Use report.md in each repo for findings, datasets, and confidence labels
• Treat all claims as computational until experimentally validated

Founder / operator?

• Focus on ENTREPRENEURSHIP lanes for venture logic, market sensemaking, systems, and public-case framing

Developer / contributor?

• Focus on TECHNOLOGY lanes for reusable tools, dashboards, reproducibility, and open infrastructure

@misc{kolisnyk2026krdlab,
  author    = {Kolisnyk, Oksana},
  title     = {K R&D Lab: Open-Source Computational Research Hub},
  year      = {2026},
  publisher = {GitHub},
  url       = {https://github.com/K-RnD-Lab},
  note      = {Three spheres: Science, Entrepreneurship, Technology. All results are hypothesis-generating.}
}

All computational models are research-grade and experimental. Results labeled simulated require validation before clinical, pharmaceutical, agricultural, or commercial application. This work does not constitute medical, agronomic, or business advice.

Built with Python · Gradio · scikit-learn · pandas · matplotlib
© 2026 Oksana Kolisnyk · KOSATIKS GROUP · MIT License