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1052 lines (1005 loc) · 52.6 KB
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Predomics — Predictive Models from Omics Data</title>
<meta name="description" content="Open-source suite for building interpretable, parsimonious classification models from high-dimensional omics data using evolutionary algorithms. Rust engine, Python bindings, web application." />
<meta property="og:title" content="Predomics — Predictive Models from Omics Data" />
<meta property="og:description" content="Discover minimal-feature biomarker signatures with evolutionary algorithms. Up to 1,000x faster with the Rust engine." />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://predomics.github.io" />
<meta property="og:image" content="https://predomics.github.io/assets/screenshots/landing.png" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:title" content="Predomics — Predictive Models from Omics Data" />
<meta name="twitter:description" content="Discover minimal-feature biomarker signatures with evolutionary algorithms." />
<meta name="twitter:image" content="https://predomics.github.io/assets/screenshots/landing.png" />
<link rel="icon" href="/favicon.ico" type="image/x-icon" />
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap" rel="stylesheet" />
<style>
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
:root {
--bg-deep: #060918;
--bg-surface: #0c1024;
--bg-card: #111631;
--bg-card-hover: #171d3e;
--accent: #4f6ef7;
--accent-light: #7b93ff;
--accent-glow: rgba(79, 110, 247, 0.15);
--accent-soft: rgba(79, 110, 247, 0.08);
--orange: #f59e0b;
--green: #10b981;
--emerald: #34d399;
--purple: #a78bfa;
--cyan: #22d3ee;
--rose: #fb7185;
--text: #e2e8f0;
--text-secondary: #94a3b8;
--text-dim: #64748b;
--border: rgba(148, 163, 184, 0.08);
--border-hover: rgba(79, 110, 247, 0.3);
--radius: 16px;
--radius-sm: 10px;
}
html { scroll-behavior: smooth; }
body {
font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
background: var(--bg-deep);
color: var(--text);
line-height: 1.6;
overflow-x: hidden;
-webkit-font-smoothing: antialiased;
}
/* ═══════════════ NAV ═══════════════ */
nav {
position: fixed; top: 0; left: 0; right: 0; z-index: 100;
display: flex; align-items: center; justify-content: space-between;
padding: 0.75rem 2rem;
background: rgba(6, 9, 24, 0.8);
backdrop-filter: blur(20px) saturate(180%);
-webkit-backdrop-filter: blur(20px) saturate(180%);
border-bottom: 1px solid var(--border);
transition: background 0.3s;
}
nav.scrolled { background: rgba(6, 9, 24, 0.95); }
.nav-brand {
display: flex; align-items: center; gap: 0.6rem;
font-size: 1.15rem; font-weight: 700; color: white; text-decoration: none;
}
.nav-brand img { height: 30px; width: 30px; object-fit: contain; }
.nav-links { display: flex; gap: 1.75rem; align-items: center; }
.nav-links a {
color: var(--text-secondary); text-decoration: none; font-size: 0.85rem;
font-weight: 500; transition: color 0.2s; position: relative;
}
.nav-links a:hover { color: white; }
.nav-links a::after {
content: ''; position: absolute; bottom: -4px; left: 0; right: 0;
height: 2px; background: var(--accent); transform: scaleX(0);
transition: transform 0.25s ease; border-radius: 1px;
}
.nav-links a:hover::after { transform: scaleX(1); }
.nav-cta {
padding: 0.45rem 1.1rem; background: var(--accent); color: white;
border-radius: 8px; text-decoration: none; font-size: 0.82rem;
font-weight: 600; transition: all 0.25s; border: 1px solid transparent;
}
.nav-cta:hover { background: var(--accent-light); transform: translateY(-1px); box-shadow: 0 4px 20px rgba(79, 110, 247, 0.3); }
/* Mobile menu */
.nav-toggle { display: none; background: none; border: none; cursor: pointer; padding: 0.5rem; }
.nav-toggle span { display: block; width: 22px; height: 2px; background: var(--text); margin: 5px 0; transition: all 0.3s; border-radius: 1px; }
/* ═══════════════ HERO ═══════════════ */
.hero {
min-height: 100vh; display: flex; align-items: center; justify-content: center;
text-align: center; padding: 7rem 2rem 4rem;
position: relative; overflow: hidden;
}
/* Animated gradient mesh background */
.hero::before {
content: ''; position: absolute; inset: 0;
background:
radial-gradient(ellipse 80% 60% at 40% -10%, rgba(79, 110, 247, 0.18), transparent 70%),
radial-gradient(ellipse 60% 50% at 90% 80%, rgba(139, 92, 246, 0.12), transparent 60%),
radial-gradient(ellipse 50% 40% at 10% 60%, rgba(16, 185, 129, 0.06), transparent 60%);
animation: heroGlow 12s ease-in-out infinite alternate;
}
@keyframes heroGlow {
0% { opacity: 0.6; transform: scale(1); }
100% { opacity: 1; transform: scale(1.05); }
}
/* Dot grid */
.hero::after {
content: ''; position: absolute; inset: 0;
background-image: radial-gradient(circle 1px, rgba(79, 110, 247, 0.15) 1px, transparent 1px);
background-size: 32px 32px;
mask-image: radial-gradient(ellipse 70% 65% at 50% 35%, black 15%, transparent 65%);
-webkit-mask-image: radial-gradient(ellipse 70% 65% at 50% 35%, black 15%, transparent 65%);
}
.hero-content { max-width: 860px; position: relative; z-index: 1; }
.hero-logo {
width: 100px; height: 100px; object-fit: contain; margin-bottom: 2rem;
filter: drop-shadow(0 0 50px rgba(79, 110, 247, 0.35));
animation: float 6s ease-in-out infinite;
}
@keyframes float {
0%, 100% { transform: translateY(0); }
50% { transform: translateY(-12px); }
}
.hero-badge {
display: inline-flex; align-items: center; gap: 0.5rem;
padding: 0.35rem 1rem; border-radius: 100px;
background: var(--accent-soft); border: 1px solid rgba(79, 110, 247, 0.2);
font-size: 0.78rem; font-weight: 500; color: var(--accent-light);
margin-bottom: 1.5rem;
}
.hero-badge .dot { width: 6px; height: 6px; border-radius: 50%; background: var(--green); animation: pulse 2s ease-in-out infinite; }
@keyframes pulse { 0%, 100% { opacity: 1; } 50% { opacity: 0.4; } }
.hero h1 {
font-size: 4rem; font-weight: 900; letter-spacing: -0.04em;
line-height: 1.05; margin-bottom: 1.25rem;
background: linear-gradient(135deg, #fff 30%, var(--accent-light) 100%);
-webkit-background-clip: text; -webkit-text-fill-color: transparent;
background-clip: text;
}
.hero .tagline {
font-size: 1.2rem; color: var(--text-secondary); max-width: 580px;
margin: 0 auto 2.5rem; font-weight: 400; line-height: 1.75;
}
.hero-buttons { display: flex; gap: 0.75rem; justify-content: center; flex-wrap: wrap; }
.btn {
padding: 0.75rem 1.75rem; border-radius: var(--radius-sm); font-weight: 600;
font-size: 0.9rem; text-decoration: none; transition: all 0.3s;
display: inline-flex; align-items: center; gap: 0.5rem; border: none; cursor: pointer;
}
.btn-primary {
background: linear-gradient(135deg, var(--accent), #6366f1);
color: white; box-shadow: 0 4px 24px rgba(79, 110, 247, 0.35);
}
.btn-primary:hover { transform: translateY(-2px); box-shadow: 0 8px 32px rgba(79, 110, 247, 0.45); }
.btn-outline {
background: rgba(255,255,255,0.03); color: var(--text);
border: 1px solid rgba(255,255,255,0.1);
}
.btn-outline:hover { border-color: var(--accent); color: var(--accent-light); background: var(--accent-soft); }
.hero-stats {
display: flex; justify-content: center; gap: 2.5rem;
margin-top: 4rem; padding-top: 2rem;
border-top: 1px solid var(--border);
}
.stat { text-align: center; }
.stat-value { font-size: 2rem; font-weight: 800; color: white; font-variant-numeric: tabular-nums; }
.stat-label { font-size: 0.75rem; color: var(--text-dim); text-transform: uppercase; letter-spacing: 0.08em; margin-top: 0.2rem; }
/* ═══════════════ SECTIONS ═══════════════ */
section { padding: 7rem 2rem; }
.container { max-width: 1200px; margin: 0 auto; }
.section-label {
font-size: 0.75rem; font-weight: 600; text-transform: uppercase;
letter-spacing: 0.12em; color: var(--accent); margin-bottom: 0.75rem;
display: inline-flex; align-items: center; gap: 0.5rem;
}
.section-label::before {
content: ''; width: 24px; height: 2px; background: var(--accent); border-radius: 1px;
}
.section-title {
font-size: 2.75rem; font-weight: 800; margin-bottom: 1rem;
letter-spacing: -0.03em; line-height: 1.15;
}
.section-desc {
font-size: 1.1rem; color: var(--text-secondary); max-width: 580px;
margin-bottom: 3.5rem; line-height: 1.75;
}
/* ═══════════════ SCROLL REVEAL ═══════════════ */
.reveal { opacity: 0; transform: translateY(40px); transition: opacity 0.8s ease, transform 0.8s ease; }
.reveal.visible { opacity: 1; transform: translateY(0); }
.reveal-stagger > * { opacity: 0; transform: translateY(24px); transition: opacity 0.5s ease, transform 0.5s ease; }
.reveal-stagger.visible > * { opacity: 1; transform: translateY(0); }
.reveal-stagger.visible > *:nth-child(1) { transition-delay: 0.05s; }
.reveal-stagger.visible > *:nth-child(2) { transition-delay: 0.1s; }
.reveal-stagger.visible > *:nth-child(3) { transition-delay: 0.15s; }
.reveal-stagger.visible > *:nth-child(4) { transition-delay: 0.2s; }
.reveal-stagger.visible > *:nth-child(5) { transition-delay: 0.25s; }
.reveal-stagger.visible > *:nth-child(6) { transition-delay: 0.3s; }
/* ═══════════════ WORKFLOW ═══════════════ */
.workflow-section { background: linear-gradient(180deg, #080b1a 0%, var(--bg-deep) 100%); }
.workflow-track { display: flex; align-items: flex-start; justify-content: center; max-width: 960px; margin: 0 auto; }
.wf-step { display: flex; flex-direction: column; align-items: center; text-align: center; padding: 1.25rem 0.75rem; flex: 1; min-width: 0; }
.wf-icon-ring {
width: 72px; height: 72px; border-radius: 50%;
background: linear-gradient(135deg, rgba(79, 110, 247, 0.12), rgba(79, 110, 247, 0.03));
border: 2px solid rgba(79, 110, 247, 0.3);
display: flex; align-items: center; justify-content: center;
position: relative; transition: all 0.4s ease;
}
.wf-step:hover .wf-icon-ring {
border-color: var(--accent-light);
box-shadow: 0 0 32px rgba(79, 110, 247, 0.25);
transform: scale(1.08);
}
.wf-icon { font-size: 1.6rem; line-height: 1; }
.wf-badge {
position: absolute; top: -3px; right: -3px;
width: 22px; height: 22px; border-radius: 50%;
background: var(--accent); color: #fff; font-size: 0.65rem;
font-weight: 700; display: flex; align-items: center; justify-content: center;
}
.wf-label { font-size: 0.95rem; font-weight: 600; margin-top: 0.75rem; margin-bottom: 0.25rem; }
.wf-text { font-size: 0.8rem; color: var(--text-secondary); line-height: 1.5; max-width: 170px; margin: 0 auto; }
.wf-connector {
display: flex; align-items: center; width: 52px; flex-shrink: 0;
padding-top: 2.2rem; position: relative;
}
.wf-line {
width: 100%; height: 2px;
background: linear-gradient(90deg, rgba(79, 110, 247, 0.4), rgba(79, 110, 247, 0.15));
border-radius: 1px;
}
.wf-line::after {
content: ''; position: absolute; right: -1px; top: calc(2.2rem - 3px);
border-top: 4px solid transparent; border-bottom: 4px solid transparent;
border-left: 6px solid rgba(79, 110, 247, 0.35);
}
.wf-dot {
position: absolute; width: 5px; height: 5px; border-radius: 50%;
background: var(--accent-light); box-shadow: 0 0 8px rgba(79, 110, 247, 0.6);
top: calc(2.2rem - 1.5px); animation: wfFlow 2.2s ease-in-out infinite;
}
.wf-dot-2 { animation-delay: 1.1s !important; }
@keyframes wfFlow {
0% { left: -5px; opacity: 0; } 12% { opacity: 1; }
88% { opacity: 1; } 100% { left: calc(52px - 5px); opacity: 0; }
}
/* ═══════════════ FEATURES ═══════════════ */
.features-section { background: var(--bg-deep); }
.features-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(320px, 1fr)); gap: 1.25rem; }
.feature-card {
background: var(--bg-card); border: 1px solid var(--border);
border-radius: var(--radius); padding: 2rem;
transition: all 0.35s ease; position: relative; overflow: hidden;
}
.feature-card::before {
content: ''; position: absolute; inset: 0;
background: radial-gradient(circle at top right, var(--accent-soft), transparent 60%);
opacity: 0; transition: opacity 0.35s;
}
.feature-card:hover::before { opacity: 1; }
.feature-card:hover {
border-color: var(--border-hover); transform: translateY(-4px);
box-shadow: 0 16px 48px rgba(0, 0, 0, 0.4);
}
.feature-icon {
width: 48px; height: 48px; border-radius: 12px;
display: flex; align-items: center; justify-content: center;
font-size: 1.4rem; margin-bottom: 1.2rem; position: relative;
}
.fi-blue { background: rgba(79, 110, 247, 0.12); color: var(--accent-light); }
.fi-orange { background: rgba(245, 158, 11, 0.12); color: var(--orange); }
.fi-green { background: rgba(16, 185, 129, 0.12); color: var(--green); }
.fi-purple { background: rgba(167, 139, 250, 0.12); color: var(--purple); }
.fi-cyan { background: rgba(34, 211, 238, 0.12); color: var(--cyan); }
.fi-rose { background: rgba(251, 113, 133, 0.12); color: var(--rose); }
.feature-card h3 { font-size: 1.1rem; font-weight: 600; margin-bottom: 0.5rem; position: relative; }
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/* ═══════════════ QUICKSTART ═══════════════ */
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/* ═══════════════ TEAM ═══════════════ */
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/* ═══════════════ FOOTER ═══════════════ */
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/* ═══════════════ RESPONSIVE ═══════════════ */
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<!-- NAV -->
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<a href="#" class="nav-brand">
<img src="/docs/logo.png" alt="Predomics" />
Predomics
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<a href="#workflow">How It Works</a>
<a href="#features">Features</a>
<a href="#screenshots">Screenshots</a>
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<img src="/docs/logo.png" alt="Predomics" class="hero-logo" />
<div class="hero-badge"><span class="dot"></span> Open Source · GPL-3.0</div>
<h1>Predictive Models<br />from Omics Data</h1>
<p class="tagline">
Discover minimal-feature biomarker signatures from high-dimensional
omics data using evolutionary algorithms — interpretable, parsimonious, and blazing fast.
</p>
<div class="hero-buttons">
<a href="#quickstart" class="btn btn-primary">Get Started →</a>
<a href="https://github.com/predomics" class="btn btn-outline">
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<div class="stat">
<div class="stat-value" data-count="1000" data-suffix="x">0x</div>
<div class="stat-label">Faster (Rust)</div>
</div>
<div class="stat">
<div class="stat-value"><1%</div>
<div class="stat-label">Feature selection</div>
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<div class="stat-label">Search algorithms</div>
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<div class="stat-label">Source & Free</div>
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</section>
<!-- WORKFLOW -->
<section class="workflow-section reveal" id="workflow">
<div class="container">
<div class="section-label">How It Works</div>
<h2 class="section-title">From Data to Discovery</h2>
<p class="section-desc">Four steps from raw omics data to validated, interpretable predictive models.</p>
<div class="workflow-track">
<div class="wf-step">
<div class="wf-icon-ring"><span class="wf-icon">⇪</span><span class="wf-badge">1</span></div>
<div class="wf-label">Upload Data</div>
<p class="wf-text">Import TSV/CSV omics matrices with training and optional test sets</p>
</div>
<div class="wf-connector"><div class="wf-line"></div><div class="wf-dot"></div><div class="wf-dot wf-dot-2"></div></div>
<div class="wf-step">
<div class="wf-icon-ring"><span class="wf-icon">⚙</span><span class="wf-badge">2</span></div>
<div class="wf-label">Configure</div>
<p class="wf-text">Choose algorithms, model languages, and fine-tune parameters</p>
</div>
<div class="wf-connector"><div class="wf-line"></div><div class="wf-dot"></div><div class="wf-dot wf-dot-2"></div></div>
<div class="wf-step">
<div class="wf-icon-ring"><span class="wf-icon">⚡</span><span class="wf-badge">3</span></div>
<div class="wf-label">Analyze</div>
<p class="wf-text">Launch evolutionary search with real-time progress monitoring</p>
</div>
<div class="wf-connector"><div class="wf-line"></div><div class="wf-dot"></div><div class="wf-dot wf-dot-2"></div></div>
<div class="wf-step">
<div class="wf-icon-ring"><span class="wf-icon">⌖</span><span class="wf-badge">4</span></div>
<div class="wf-label">Explore</div>
<p class="wf-text">Inspect models, feature importance, jury voting, export reports</p>
</div>
</div>
</div>
</section>
<!-- FEATURES -->
<section class="features-section reveal" id="features">
<div class="container">
<div class="section-label">Why Predomics</div>
<h2 class="section-title">Designed for Discovery</h2>
<p class="section-desc">From raw omics matrices to publication-ready models in minutes, with full transparency at every step.</p>
<div class="features-grid reveal-stagger">
<div class="feature-card">
<div class="feature-icon fi-blue">⚖</div>
<h3>Parsimonious Models</h3>
<p>Discovers minimal feature sets (binary, ternary, ratio) that achieve high classification accuracy. Models you can interpret and publish.</p>
</div>
<div class="feature-card">
<div class="feature-icon fi-orange">🧬</div>
<h3>Evolutionary Search</h3>
<p>Genetic algorithms, beam search, and MCMC heuristics explore the feature space efficiently to find optimal biomarker signatures.</p>
</div>
<div class="feature-card">
<div class="feature-icon fi-green">🗳</div>
<h3>Jury Voting</h3>
<p>Ensemble of expert models with majority voting, rejection capability, concordance analysis, and per-sample confidence scores.</p>
</div>
<div class="feature-card">
<div class="feature-icon fi-purple">📊</div>
<h3>Rich Visualizations</h3>
<p>Prevalence plots, heatmaps, violin plots, SHAP explanations, PCoA, co-presence networks, and 30+ interactive chart types.</p>
</div>
<div class="feature-card">
<div class="feature-icon fi-cyan">🔄</div>
<h3>Cross-validation</h3>
<p>Built-in k-fold CV with generation-level tracking. Monitor train vs test AUC, complexity, and fit in real time.</p>
</div>
<div class="feature-card">
<div class="feature-icon fi-rose">⚡</div>
<h3>Blazing Fast</h3>
<p>Core engine rewritten in Rust with Python bindings. Up to 1,000x faster than the original R implementation.</p>
</div>
</div>
</div>
</section>
<!-- SCREENSHOTS -->
<section class="screenshots-section reveal" id="screenshots">
<div class="container">
<div class="section-label">See It In Action</div>
<h2 class="section-title">Web Application</h2>
<p class="section-desc">A full-stack interface for running analyses, exploring results, and sharing with collaborators.</p>
<div class="screenshot-tabs">
<button class="screenshot-tab active" data-target="dashboard">Dashboard</button>
<button class="screenshot-tab" data-target="data">Data Explorer</button>
<button class="screenshot-tab" data-target="parameters">Parameters</button>
<button class="screenshot-tab" data-target="results">Results</button>
<button class="screenshot-tab" data-target="best_model">Best Model</button>
<button class="screenshot-tab" data-target="jury">Jury</button>
<button class="screenshot-tab" data-target="population">Population</button>
<button class="screenshot-tab" data-target="copresence">Co-presence</button>
<button class="screenshot-tab" data-target="comparative">Comparative</button>
</div>
<div class="screenshot-frame">
<div id="ss-dashboard" class="screenshot-item active"><img src="/assets/screenshots/dashboard.png" alt="Dashboard with global statistics, recent jobs, and project overview" loading="lazy" /></div>
<div id="ss-data" class="screenshot-item"><img src="/assets/screenshots/data.png" alt="Data explorer with prevalence plots and feature filtering" loading="lazy" /></div>
<div id="ss-parameters" class="screenshot-item"><img src="/assets/screenshots/parameters.png" alt="Parameter configuration for algorithms and model types" loading="lazy" /></div>
<div id="ss-results" class="screenshot-item"><img src="/assets/screenshots/results.png" alt="Job results table with AUC scores and metrics" loading="lazy" /></div>
<div id="ss-best_model" class="screenshot-item"><img src="/assets/screenshots/best_model.png" alt="Best model with coefficients, feature importance, and SHAP" loading="lazy" /></div>
<div id="ss-jury" class="screenshot-item"><img src="/assets/screenshots/jury.png" alt="Jury voting with confusion matrices and vote heatmap" loading="lazy" /></div>
<div id="ss-population" class="screenshot-item"><img src="/assets/screenshots/population.png" alt="Population of models with feature heatmap and violin plots" loading="lazy" /></div>
<div id="ss-copresence" class="screenshot-item"><img src="/assets/screenshots/copresence.png" alt="Co-presence analysis with network visualization" loading="lazy" /></div>
<div id="ss-comparative" class="screenshot-item"><img src="/assets/screenshots/comparative.png" alt="Comparative analysis across multiple jobs" loading="lazy" /></div>
</div>
</div>
</section>
<!-- USE CASES -->
<section class="usecases-section reveal" id="usecases">
<div class="container">
<div class="section-label">Applications</div>
<h2 class="section-title">Real-World Use Cases</h2>
<p class="section-desc">Predomics has been applied to microbiome and multi-omics classification problems across clinical contexts.</p>
<div class="usecase-grid reveal-stagger">
<div class="usecase-card">
<div class="uc-header"><span class="uc-icon">⚕</span><h3>Cirrhosis Prediction</h3></div>
<p class="uc-desc">Predict liver cirrhosis from gut microbiome composition using metagenomic species abundance profiles.</p>
<div class="uc-stats"><span class="uc-stat"><strong>0.94</strong> AUC</span><span class="uc-stat"><strong>k=8</strong> features</span><span class="uc-stat"><strong>232</strong> samples</span></div>
<div class="uc-ref">Qin et al., Nature 2014</div>
</div>
<div class="usecase-card">
<div class="uc-header"><span class="uc-icon">⚄</span><h3>Cancer Classification</h3></div>
<p class="uc-desc">Classify colorectal cancer status from stool metagenomic data using sparse ternary models.</p>
<div class="uc-stats"><span class="uc-stat"><strong>0.92</strong> AUC</span><span class="uc-stat"><strong>k=12</strong> features</span><span class="uc-stat"><strong>156</strong> samples</span></div>
<div class="uc-ref">Zeller et al., Mol. Syst. Biol. 2014</div>
</div>
<div class="usecase-card">
<div class="uc-header"><span class="uc-icon">⌘</span><h3>Treatment Response</h3></div>
<p class="uc-desc">Predict immunotherapy response from baseline gut microbiome in melanoma patients.</p>
<div class="uc-stats"><span class="uc-stat"><strong>0.87</strong> AUC</span><span class="uc-stat"><strong>k=5</strong> features</span><span class="uc-stat"><strong>112</strong> samples</span></div>
<div class="uc-ref">Gopalakrishnan et al., Science 2018</div>
</div>
<div class="usecase-card">
<div class="uc-header"><span class="uc-icon">☣</span><h3>Metabolic Disease</h3></div>
<p class="uc-desc">Identify type 2 diabetes biomarkers from metagenome-wide association studies.</p>
<div class="uc-stats"><span class="uc-stat"><strong>0.89</strong> AUC</span><span class="uc-stat"><strong>k=10</strong> features</span><span class="uc-stat"><strong>345</strong> samples</span></div>
<div class="uc-ref">Karlsson et al., Nature 2013</div>
</div>
</div>
</div>
</section>
<!-- SUITE -->
<section class="suite-section reveal" id="suite">
<div class="container">
<div class="section-label">Ecosystem</div>
<h2 class="section-title">The Predomics Suite</h2>
<p class="section-desc">Three complementary tools, one unified approach to interpretable omics classification.</p>
<div class="suite-grid reveal-stagger">
<div class="suite-card rust">
<span class="suite-badge badge-rust">Rust + Python</span>
<h3>gpredomics</h3>
<p>High-performance ML engine rewritten in Rust — up to 1,000x faster than the original R package. Python bindings via gpredomicspy for notebooks and scripts.</p>
<a href="https://github.com/predomics/gpredomics" class="suite-link">View Repository →</a>
</div>
<div class="suite-card web">
<span class="suite-badge badge-web">FastAPI + Vue.js</span>
<h3>PredomicsApp</h3>
<p>Full-stack web application for running analyses, exploring results with 30+ interactive visualizations, and sharing with collaborators. Deploy anywhere with Docker.</p>
<a href="https://github.com/predomics/predomicsapp-web" class="suite-link">View Repository →</a>
</div>
<div class="suite-card r">
<span class="suite-badge badge-r">R Package</span>
<h3>gpredomicsR</h3>
<p>R bindings for the gpredomics Rust engine. Bringing the 1,000x Rust speedup to the R ecosystem with a familiar interface.</p>
<a href="https://github.com/predomics/predomicspkg" class="suite-link">View Repository →</a>
</div>
</div>
</div>
</section>
<!-- QUICKSTART -->
<section class="quickstart-section reveal" id="quickstart">
<div class="container">
<div class="section-label">Get Started</div>
<h2 class="section-title">Up and Running in Minutes</h2>
<p class="section-desc">Choose your preferred way to use Predomics.</p>
<div class="code-tabs reveal-stagger">
<div class="code-card">
<h3>🐳 Docker (Web App)</h3>
<div class="code-block" id="code-docker">
<button class="copy-btn" data-target="code-docker">Copy</button>
<span class="cmd">git clone</span> https://github.com/predomics/predomicsapp-web.git<br/>
<span class="cmd">cd</span> predomicsapp-web<br/>
<span class="cmd">docker compose up -d</span><br/><br/>
<span class="comment"># Open http://localhost:8001</span>
</div>
</div>
<div class="code-card">
<h3>🐍 Python</h3>
<div class="code-block" id="code-python">
<button class="copy-btn" data-target="code-python">Copy</button>
<span class="kw">import</span> gpredomicspy<br/><br/>
param = gpredomicspy.<span class="cmd">Param</span>()<br/>
param.<span class="cmd">load</span>(<span class="str">"params.yaml"</span>)<br/>
experiment = gpredomicspy.<span class="cmd">fit</span>(param)<br/>
experiment.<span class="cmd">display_results</span>()
</div>
</div>
<div class="code-card">
<h3>📊 R (gpredomicsR)</h3>
<div class="code-block" id="code-r">
<button class="copy-btn" data-target="code-r">Copy</button>
devtools::<span class="cmd">install_github</span>(<span class="str">"predomics/gpredomicsR"</span>)<br/><br/>
<span class="kw">library</span>(gpredomicsR)<br/>
result <- <span class="cmd">gpredomics.fit</span>(params)<br/>
</div>
</div>
</div>
</div>
</section>
<!-- PUBLICATION -->
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<div class="container">
<div class="section-label">Research</div>
<h2 class="section-title">Publication</h2>
<div class="pub-card">
<div class="journal">GigaScience, Volume 9, Issue 3, March 2020</div>
<div class="title">Interpretable and accurate prediction models for metagenomics data</div>
<div class="authors">Prifti E., Chevaleyre Y., Hanczar B., Belda E., Danchin A., Clément K., & Zucker J.-D.</div>
<a href="https://doi.org/10.1093/gigascience/giaa010" class="doi-link" target="_blank" rel="noopener">DOI: 10.1093/gigascience/giaa010 →</a>
</div>
</div>
</section>
<!-- TEAM -->
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<div class="container">
<div class="section-label">People</div>
<h2 class="section-title">Team & Contributors</h2>
<p class="section-desc">The researchers, developers, and students behind Predomics.</p>
<div class="team-groups">
<div>
<div class="team-group-label">Core Team</div>
<div class="team-grid reveal-stagger">
<div class="team-member lead">
<div class="tm-name">Edi Prifti</div>
<div class="tm-role">Creator & Lead</div>
<div class="tm-affil">IRD / UMMISCO</div>
<div class="tm-desc">Created Predomics in 2015. Designed the evolutionary algorithms, Family of Best Models, and the web application.</div>
<a href="https://orcid.org/0000-0001-8861-1305" class="tm-orcid" target="_blank" rel="noopener">ORCID: 0000-0001-8861-1305</a>
</div>
<div class="team-member lead">
<div class="tm-name">Jean-Daniel Zucker</div>
<div class="tm-role">Co-Creator</div>
<div class="tm-affil">IRD / UMMISCO</div>
<div class="tm-desc">Co-led the work since 2015. Proposed the beam search heuristic and founding ideas.</div>
<a href="https://orcid.org/0000-0002-5597-7922" class="tm-orcid" target="_blank" rel="noopener">ORCID: 0000-0002-5597-7922</a>
</div>
<div class="team-member">
<div class="tm-name">Yann Chevaleyre</div>
<div class="tm-role">Mathematical Optimization</div>
<div class="tm-affil">LIPN</div>
<div class="tm-desc">Worked on balances and the TerDa optimization method.</div>
</div>
<div class="team-member">
<div class="tm-name">Blaise Hanczar</div>
<div class="tm-role">Model Selection</div>
<div class="tm-affil">IBISC</div>
<div class="tm-desc">Contributed to best model selection and general framework ideas.</div>
</div>
<div class="team-member">
<div class="tm-name">Eugeni Belda</div>
<div class="tm-role">Interpretability & Validation</div>
<div class="tm-affil">IRD</div>
<div class="tm-desc">Signature interpretability, microbiome applications, and extensive dataset testing.</div>
<a href="https://orcid.org/0000-0003-4307-5072" class="tm-orcid" target="_blank" rel="noopener">ORCID: 0000-0003-4307-5072</a>
</div>
</div>
</div>
<div>
<div class="team-group-label">gpredomics Rust Engine</div>
<div class="team-grid reveal-stagger">
<div class="team-member">
<div class="tm-name">Louison Lesage</div>
<div class="tm-role">Lead Rust Developer</div>
<div class="tm-affil">GMT Science</div>
<div class="tm-desc">Led the Rust engine rewrite delivering up to 1,000x performance gains.</div>
<a href="https://orcid.org/0009-0000-0252-6311" class="tm-orcid" target="_blank" rel="noopener">ORCID: 0009-0000-0252-6311</a>
</div>
<div class="team-member">
<div class="tm-name">Raynald de Lahondès</div>
<div class="tm-role">Rust Developer</div>
<div class="tm-affil">GMT Science</div>
<div class="tm-desc">Engine architecture and optimization for the Rust implementation.</div>
<a href="https://orcid.org/0009-0000-2862-9589" class="tm-orcid" target="_blank" rel="noopener">ORCID: 0009-0000-2862-9589</a>
</div>
<div class="team-member">
<div class="tm-name">Vadim Puller</div>
<div class="tm-role">Scientific Developer</div>
<div class="tm-affil">GMT Science</div>
<div class="tm-desc">Algorithm implementation and validation in the Rust engine.</div>
<a href="https://orcid.org/0000-0002-3900-8283" class="tm-orcid" target="_blank" rel="noopener">ORCID: 0000-0002-3900-8283</a>
</div>
</div>
</div>
<div>
<div class="team-group-label">Contributors</div>
<div class="team-grid reveal-stagger">
<div class="team-member">
<div class="tm-name">Lucas Robin</div>
<div class="tm-desc">TerGa2 algorithm implementation and code optimization (2016).</div>
</div>
<div class="team-member">
<div class="tm-name">Shasha Cui</div>
<div class="tm-desc">Feature importance and model stability analysis (2017).</div>
</div>
<div class="team-member">
<div class="tm-name">Magali Cousin Thorez</div>
<div class="tm-desc">Signature simplification and microbial ecosystem exploration (2019).</div>
</div>
<div class="team-member">
<div class="tm-name">Youcef Sklab</div>
<div class="tm-desc">Co-led student projects for the R Shiny PredomicsApp.</div>
</div>
<div class="team-member">
<div class="tm-name">Gaspar Roy</div>
<div class="tm-desc">Evolved version of the R Shiny application (2023).</div>
</div>
<div class="team-member">
<div class="tm-name">Fabien Kambu</div>
<div class="tm-desc">Testing, documentation, and deployment of PredomicsApp.</div>
</div>
</div>
</div>
</div>
</div>
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Open-source suite for building interpretable, parsimonious classification
models from high-dimensional omics data using evolutionary algorithms.
Developed at IRD / UMMISCO.
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<h4>Suite</h4>
<a href="https://github.com/predomics/gpredomics">gpredomics (Rust)</a>
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