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Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction

Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.

Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.

The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.

workflow

Installation

Flexynesis requires Python 3.11+.
You can install the latest release from PyPI:

pip install flexynesis

Citing our work

In order to refer to our work, please cite our manuscript published at Nature Communications.

Getting started with Flexynesis

Command-line tutorial

Jupyter notebooks for interactive usage

Running Flexynesis on Galaxy

Docker

Benchmarks

For the latest benchmark results see: https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html

The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks

Documentation

Flexynesis Documentation was generated using mkdocs

pip install mkdocstrings[python]
mkdocs build --clean

Contact

For questions, suggestions, or collaborations: Open an issue or create a discussion.

License

Flexynesis is released under a PolyForm Noncommercial License 1.0.0. Please contact us for permission to use it for commercial purposes. © 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).

About

A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
workflow
Installation
Flexynesis requires Python 3.11+.

You can install the latest release from PyPI:
pip install flexynesis
Citing our work
In order to refer to our work, please cite our manuscript published at Nature Communications.
Getting started with Flexynesis
Command-line tutorial
Jupyter notebooks for interactive usage
Running Flexynesis on Galaxy
Docker
Benchmarks
For the latest benchmark results see:
https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
Documentation
Flexynesis Documentation was generated using mkdocs
pip install mkdocstrings[python]
mkdocs build --clean

Contact
For questions, suggestions, or collaborations: Open an issue or create a discussion.
License
Flexynesis is released under a PolyForm Noncommercial License 1.0.0.
Please contact us for permission to use it for commercial purposes.
© 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).
About
A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
workflow
Installation
Flexynesis requires Python 3.11+.

You can install the latest release from PyPI:
pip install flexynesis
Citing our work
In order to refer to our work, please cite our manuscript published at Nature Communications.
Getting started with Flexynesis
Command-line tutorial
Jupyter notebooks for interactive usage
Running Flexynesis on Galaxy
Docker
Benchmarks
For the latest benchmark results see:
https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
Documentation
Flexynesis Documentation was generated using mkdocs
pip install mkdocstrings[python]
mkdocs build --clean

Contact
For questions, suggestions, or collaborations: Open an issue or create a discussion.
License
Flexynesis is released under a PolyForm Noncommercial License 1.0.0.
Please contact us for permission to use it for commercial purposes.
© 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).
About
A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
workflow
Installation
Flexynesis requires Python 3.11+.

You can install the latest release from PyPI:
pip install flexynesis
Citing our work
In order to refer to our work, please cite our manuscript published at Nature Communications.
Getting started with Flexynesis
Command-line tutorial
Jupyter notebooks for interactive usage
Running Flexynesis on Galaxy
Docker
Benchmarks
For the latest benchmark results see:
https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
Documentation
Flexynesis Documentation was generated using mkdocs
pip install mkdocstrings[python]
mkdocs build --clean

Contact
For questions, suggestions, or collaborations: Open an issue or create a discussion.
License
Flexynesis is released under a PolyForm Noncommercial License 1.0.0.
Please contact us for permission to use it for commercial purposes.
© 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).
About
A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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logo
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Docker Pulls		Ubuntu



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Ubuntu



macOS



Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
workflow
Installation
Flexynesis requires Python 3.11+.

You can install the latest release from PyPI:
pip install flexynesis
Citing our work
In order to refer to our work, please cite our manuscript published at Nature Communications.
Getting started with Flexynesis
Command-line tutorial
Jupyter notebooks for interactive usage
Running Flexynesis on Galaxy
Docker
Benchmarks
For the latest benchmark results see:
https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
Documentation
Flexynesis Documentation was generated using mkdocs
pip install mkdocstrings[python]
mkdocs build --clean

Contact
For questions, suggestions, or collaborations: Open an issue or create a discussion.
License
Flexynesis is released under a PolyForm Noncommercial License 1.0.0.
Please contact us for permission to use it for commercial purposes.
© 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).
About
A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
workflow
Installation
Flexynesis requires Python 3.11+.

You can install the latest release from PyPI:
pip install flexynesis
Citing our work
In order to refer to our work, please cite our manuscript published at Nature Communications.
Getting started with Flexynesis
Command-line tutorial
Jupyter notebooks for interactive usage
Running Flexynesis on Galaxy
Docker
Benchmarks
For the latest benchmark results see:
https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
Documentation
Flexynesis Documentation was generated using mkdocs
pip install mkdocstrings[python]
mkdocs build --clean

Contact
For questions, suggestions, or collaborations: Open an issue or create a discussion.
License
Flexynesis is released under a PolyForm Noncommercial License 1.0.0.
Please contact us for permission to use it for commercial purposes.
© 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).
About
A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Docker Pulls		Ubuntu



macOS



Ubuntu



macOS



Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
workflow
Installation
Flexynesis requires Python 3.11+.

You can install the latest release from PyPI:
pip install flexynesis
Citing our work
In order to refer to our work, please cite our manuscript published at Nature Communications.
Getting started with Flexynesis
Command-line tutorial
Jupyter notebooks for interactive usage
Running Flexynesis on Galaxy
Docker
Benchmarks
For the latest benchmark results see:
https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
Documentation
Flexynesis Documentation was generated using mkdocs
pip install mkdocstrings[python]
mkdocs build --clean

Contact
For questions, suggestions, or collaborations: Open an issue or create a discussion.
License
Flexynesis is released under a PolyForm Noncommercial License 1.0.0.
Please contact us for permission to use it for commercial purposes.
© 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).
About
A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Flexynesis: deep learning toolkit for interpretable multi-omics integration and clinical outcome prediction
Flexynesis is a deep learning suite for multi-omics data integration, designed for (pre-)clinical endpoint prediction. It supports diverse neural architectures — from fully connected networks and supervised variational autoencoders to graph convolutional and multi-triplet models — with flexible options for omics layer fusion, automated feature selection, and hyperparameter optimization.
Built with interpretability in mind, Flexynesis incorporates integrated gradients (via Captum) for marker discovery, helping researchers move beyond black-box models.
The framework is continuously benchmarked on public datasets, particularly in oncology, and has been applied to tasks such as drug response prediction in patients and preclinical models (cell lines, PDXs), cancer subtype classification, and clinically relevant outcomes in regression, classification, survival, and cross-modality settings.
workflow
Installation
Flexynesis requires Python 3.11+.

You can install the latest release from PyPI:
pip install flexynesis
Citing our work
In order to refer to our work, please cite our manuscript published at Nature Communications.
Getting started with Flexynesis
Command-line tutorial
Jupyter notebooks for interactive usage
Running Flexynesis on Galaxy
Docker
Benchmarks
For the latest benchmark results see:
https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/dashboard.html
The code for the benchmarking pipeline is at: https://github.com/BIMSBbioinfo/flexynesis-benchmarks
Documentation
Flexynesis Documentation was generated using mkdocs
pip install mkdocstrings[python]
mkdocs build --clean

Contact
For questions, suggestions, or collaborations: Open an issue or create a discussion.
License
Flexynesis is released under a PolyForm Noncommercial License 1.0.0.
Please contact us for permission to use it for commercial purposes.
© 2025 Bioinformatics and Omic Data Science Platform, Max Delbrück Center for Molecular Medicine (MDC).
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A deep-learning based multi-modal data integration suite that aims to achieve synesis in a flexible manner
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