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Redesign landing page with project logo and updated documentation
Add predomicslogo7 as site logo and favicon. Rewrite landing page with project overview, feature table, suite architecture, and getting started guides. Update all documentation pages with proper Jekyll front matter and accurate content for the current multi-repo architecture. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎_config.yml‎

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title: Predomics
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description: Predictive Models from Omics Data
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theme: just-the-docs
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logo: /docs/logo.png
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favicon_ico: /favicon.ico
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navigation:
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- title: "Package Documentation"
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url: /docs/index.html
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color_scheme: dark
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aux_links:
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"GitHub":
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- "https://github.com/predomics"
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nav_external_links:
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- title: Web Application
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url: https://github.com/predomics/predomicsapp-web
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- title: R Package
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url: https://github.com/predomics/predomicspkg
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- title: Rust Engine
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url: https://github.com/predomics/gpredomics

‎docs/Authors.md‎

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---
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layout: default
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title: Authors
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parent: Documentation
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nav_order: 8
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---
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# Authors
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Predomics was created by:

‎docs/Contact.md‎

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---
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layout: default
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title: Contact
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parent: Documentation
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nav_order: 9
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---
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# Contact
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If you have any questions or feedback, please contact us at:

‎docs/Contributing.md‎

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layout: default
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title: Contributing
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parent: Documentation
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nav_order: 10
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---
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# Contributing to Predomics
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We welcome contributions to the Predomics project! Here’s how you can help:

‎docs/FAQs.md‎

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---
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layout: default
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title: FAQs
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parent: Documentation
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nav_order: 7
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---
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# Frequently Asked Questions
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**Q1: How do I install Predomics?**

‎docs/Features.md‎

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---
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layout: default
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title: Features
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parent: Documentation
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nav_order: 3
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---
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# Features
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Predomics offers a wide array of features:
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## Model Languages
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- **Binary (bin)** -- Features contribute as 0 or 1 (presence/absence)
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- **Ternary (ter)** -- Features contribute as -1, 0, or +1 (direction matters)
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- **Ratio** -- Score is computed as a ratio of two feature groups
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## Search Algorithms
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- **Genetic Algorithm (GA)** -- Population-based evolutionary search with crossover, mutation, and selection
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- **Beam Search** -- Deterministic heuristic that grows signatures incrementally
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- **Data-driven (Da)** -- Analytical approach for optimal feature selection
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## Jury / Ensemble Voting
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- Build an ensemble of expert models from the best individuals across generations
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- **Majority voting** -- Weighted expert consensus with configurable threshold
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- **Consensus voting** -- Requires minimum agreement level to predict
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- **Rejection** -- Samples below confidence threshold are abstained (class 2)
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- Per-sample vote matrix visualization and concordance analysis
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1. **Feature 1**: Description of Feature 1.
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2. **Feature 2**: Description of Feature 2.
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3. **Feature 3**: Description of Feature 3.
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## Evaluation
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- K-fold cross-validation with AUC, accuracy, sensitivity, specificity
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- Train/test split with independent evaluation
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- Confusion matrices with rejection class support
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- Feature importance ranking via permutation
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Explore each feature to make the most out of Predomics.
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## Web Application
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- Project and dataset management with user authentication
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- Parameter configuration with admin-level defaults
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- Job queue with real-time progress monitoring
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- Interactive results: heatmaps, violin plots, generation tracking, population explorer
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- Jury visualization: concordance charts, sample predictions, vote matrix heatmap
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- Project sharing between users (viewer/editor roles)

‎docs/Installation.md‎

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---
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layout: default
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title: Installation
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parent: Documentation
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nav_order: 2
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---
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# Installation
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Follow these steps to install the Predomics package:
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## Web Application (Docker)
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1. Install R from [CRAN](https://cran.r-project.org/).
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2. Install the required dependencies:
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The fastest way to get started:
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```bash
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git clone https://github.com/predomics/predomicsapp-web.git
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cd predomicsapp-web
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docker compose up -d
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```
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Open [http://localhost:8001](http://localhost:8001) in your browser. Default credentials are created on first launch.
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## R Package
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1. Install R from [CRAN](https://cran.r-project.org/).
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2. Install the package and dependencies:
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```r
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devtools::install_github("predomics/predomicspkg")
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## install dependencies
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install.packages(c("doSNOW", "foreach", "snow", "doRNG", "gtools", "glmnet", "pROC", "viridis", "kernlab", "randomForest","effsize"))
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# Install dependencies
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install.packages(c("doSNOW", "foreach", "snow", "doRNG", "gtools",
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"glmnet", "pROC", "viridis", "kernlab",
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"randomForest", "effsize"))
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if (!requireNamespace("BiocManager", quietly = TRUE))
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install.packages("BiocManager")
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BiocManager::install("BioQC")
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# install.packages("testthat")
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# install.packages("roxygen2")
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```
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Once eveything is installed un the check ...
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## Python Bindings (gpredomicspy)
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Requires Rust toolchain for compilation:
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```bash
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# Install Rust
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
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# Clone and build
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git clone https://github.com/predomics/gpredomics.git
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git clone https://github.com/predomics/gpredomicspy.git
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cd gpredomicspy
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pip install maturin
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maturin develop --release
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```

‎docs/Screenshots.md‎

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# Screenshots
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Here are some screenshots to give you an overview of Predomics:
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---
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layout: default
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title: Screenshots
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parent: Documentation
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nav_order: 5
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---
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![Screenshot 1](path/to/screenshot1.png)
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Caption for Screenshot 1.
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# Screenshots
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![Screenshot 2](path/to/screenshot2.png)
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Caption for Screenshot 2.
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Screenshots of the PredomicsApp web interface will be added here.

‎docs/Technologies.md‎

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# Technologies Used
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---
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layout: default
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title: Technologies
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parent: Documentation
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nav_order: 6
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---
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Predomics is built with the following technologies:
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# Technologies
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- R
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- Other Libraries
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## Rust Engine (gpredomics)
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- **Rust** -- Core ML algorithms, evolutionary search, jury voting
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- **PyO3 / Maturin** -- Python bindings for seamless integration
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We chose these technologies for their ...
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## Web Application (predomicsapp-web)
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- **FastAPI** (Python) -- Async REST API backend
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- **Vue.js 3** -- Reactive frontend with Composition API
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- **Plotly.js** -- Interactive data visualizations
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- **PostgreSQL 16** -- User, project, and job persistence
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- **SQLAlchemy** -- Async ORM with migration support
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- **Docker Compose** -- One-command deployment
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## R Package (predomicspkg)
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- **R** -- Original algorithm implementations
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- **doSNOW / foreach** -- Parallel computation
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- **pROC / glmnet** -- Statistical modeling
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- **viridis** -- Data visualization

‎docs/Usage.md‎

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layout: default
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title: Usage
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parent: Documentation
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---
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# Usage
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Here's how you can get started with Predomics:
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## Web Application
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1. Create a project and upload your datasets (Xtrain, Ytrain, optionally Xtest, Ytest)
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2. Configure parameters (language, algorithm, population size, etc.)
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3. Launch an analysis job and monitor progress in real time
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4. Explore results: metrics, population, heatmaps, jury voting
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## R Package
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```
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R
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```r
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library(predomics)
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result <- mainFunction(data)
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print(result)
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```
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## Python (gpredomicspy)
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```python
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import gpredomicspy
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# Load parameters from YAML
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param = gpredomicspy.Param()
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param.load("params.yaml")
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# Run the evolutionary search
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experiment = gpredomicspy.fit(param)
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# Display results with jury voting
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experiment.display_results()
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# Access the best individual
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best = experiment.best_population().best()
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print(best.get_metrics())
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print(best.get_features())
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```

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