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<p class="eyebrow">
Unified human gene representations
</p>
<h1>Gene Embedding Hub</h1>
<p class="lead">
Browse standardized gene-level representations spanning
protein sequence, DNA sequence, RNA sequence, expression,
biomedical text, functional data, networks, ontologies,
regulatory genomics, knowledge graphs, single-cell, and
multimodal representations.
</p>
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<a
class="button primary"
href="#contribute"
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Contribute an embedding
</a>
<a
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href="https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset"
target="_blank"
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>
Open Core Dataset
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<p class="notice">
Community contributions are automatically checked for
compliance with the technical policy. Inclusion is not
independent scientific or legal endorsement.
</p>
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</header>
<main>
<section
class="stats"
aria-label="Catalog summary"
>
<article>
<strong id="stat-count">—</strong>
<span>embedding sets</span>
</article>
<article>
<strong id="stat-genes">—</strong>
<span>maximum genes</span>
</article>
<article>
<strong id="stat-dims">—</strong>
<span>dimension range</span>
</article>
</section>
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<p class="section-label">
Community contribution
</p>
<h2>Submit a gene embedding</h2>
<p class="contribute-intro">
Prepare one standardized package and upload it directly to
the library-owned Hugging Face Dataset as a Pull Request.
</p>
<div class="instruction-grid">
<article class="instruction-card">
<span class="step-number">1</span>
<h3>Prepare one folder</h3>
<pre><code>&lt;embedding_id&gt;/
├── embeddings.npz
├── genes.tsv
├── README.md
└── submission.yaml</code></pre>
<p>
The embedding ID must contain 3–80 lowercase letters,
numbers, dots, underscores, or hyphens and must begin
with a letter or number.
</p>
<p>
A <code>metadata.json</code> file is automatically
generated and saved in the embedding's folder after
acceptance.
</p>
</article>
<article class="instruction-card">
<span class="step-number">2</span>
<h3>Check the package contract</h3>
<ul>
<li>
<code>embeddings.npz</code> contains exactly one
two-dimensional <code>float32</code> array named
<code>embeddings</code>.
</li>
<li>
<code>genes.tsv</code> contains one unique
<code>ensembl_gene_id</code> column whose rows match
the matrix rows.
</li>
<li>
<code>submission.yaml</code> provides the scientific
context and provenance needed to understand the
embedding and how it was produced.
</li>
<li>
<code>README.md</code> documents how the representation
was produced.
</li>
</ul>
<p>
<a
class="button"
href="gene-mapping-tutorial.html"
>
Open gene mapping tutorial
</a>
</p>
</article>
<article class="instruction-card">
<span class="step-number">3</span>
<h3>Complete the submission template</h3>
<p>
Download <code>submission.yaml</code>, replace every
placeholder and keep the embedding ID identical to the
folder name. Submitted values are checked automatically
against the package.
</p>
</article>
<article class="instruction-card">
<span class="step-number">4</span>
<h3>Upload as a Pull Request</h3>
<ol>
<li>
Open the Hugging Face uploader using the button below.
</li>
<li>
Upload the folder
<code>&lt;embedding_id&gt;</code>.
</li>
<li>
Select <strong>Open as a pull request</strong>.
</li>
</ol>
</article>
<article class="instruction-card full-width">
<span class="step-number">5</span>
<h3>Automatic validation and publication</h3>
<p>
The package structure, metadata consistency, Ensembl
identifiers, matrix shape, numeric validity, licensing,
provenance, and attestations are checked automatically.
</p>
<p>
A strict PASS is stored in the central Dataset and added
to this catalog.
</p>
</article>
</div>
<div class="button-row">
<a
class="button primary"
href="https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-contributions-staging/upload/main"
target="_blank"
rel="noopener noreferrer"
>
Open Hugging Face uploader
</a>
<a
class="button"
href="submission.yaml"
download
>
Download submission template
</a>
<a
class="button"
href="submission.example.yaml"
download
>Download completed example</a>
</div>
<details class="cli-alternative">
<summary>Command-line alternative example</summary>
<p>
Before running this example, replace
<code>my_embedding_v1</code> with the name of your existing
local embedding folder in both path arguments.
</p>
<pre><code>hf upload \
BoevaLab/Gene-Embedding-Hub-contributions-staging \
./my_embedding_v1 \
my_embedding_v1 \
--repo-type dataset \
--create-pr \
--commit-message "Submit gene embedding"</code></pre>
</details>
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