add interpretation pipeline in home

.gitattributes

@@ -44,3 +44,4 @@ notebooks_pipelines/bigwig_outputs/HepG2_DNAse.bw filter=lfs diff=lfs merge=lfs
 notebooks_pipelines/bigwig_outputs/HepG2_H3k4me3.bw filter=lfs diff=lfs merge=lfs -text
 notebooks_pipelines/bigwig_outputs/HepG2_RNA_seq.bw filter=lfs diff=lfs merge=lfs -text
 notebooks_pipelines/bigwig_outputs/K562_CTCF.bw filter=lfs diff=lfs merge=lfs -text
+assets/saliency_example.png filter=lfs diff=lfs merge=lfs -text

tabs/home.html

@@ -132,7 +132,42 @@ print(out.logits.shape)       # (B, L, V = 11)
 </code></pre></div>
     <p>Model embeddings can be used for fine-tuning on downstream tasks.</p>
 
-    <p style="margin-top: 40px;">TO DO: add pipeline for fine-tuning on functional tracks or genome annotation.</p>
+    <h2 style="margin-top: 40px;">🔍 Model interpretation</h2>
+    <p>Here is an example of how to use the interpretation pipeline for multi-scale analysis of DNA sequences:</p>
+    <div class="code"><pre><code class="language-python">from transformers import pipeline
+import torch
+import matplotlib.pyplot as plt
+
+model_name = "InstaDeepAI/NTv3_650M_post"
+
+# Build interpretation pipeline
+ntv3_interpret = pipeline(
+    "ntv3-interpret",
+    model=model_name,
+    trust_remote_code=True,
+    device=0 if torch.cuda.is_available() else -1,
+)
+
+# Run interpretation on a given genomic region with tracks, annotations, attention, and saliency
+result = ntv3_interpret(
+    {"chrom": "chr11", "start": 5_253_561, "end": 5_286_329, "species": "human"},
+    output_attention=True,
+    output_saliency=True,
+    saliency_track_id="ENCSR000EFT",  # K562 GATA1 ChIP-seq
+    plot=True,  # plot predictons on tracks and annotations
+    tracks_to_plot={"K562 RNA-seq": "ENCSR056HPM", "K562 GATA1": "ENCSR000EFT"},
+    elements_to_plot=["exon", "promoter_Tissue_specific"],
+)
+
+# Access attention map results
+result.plot_attention()  # attention map (last layer)
+plt.show()
+
+# Access saliency scores results
+result.plot_saliency(window_size=128)
+plt.show()
+</code></pre></div>
+<img src="assets/saliency_example.png" alt="Output tracks visualization" style="max-width: 100%; margin-top: 20px;" />
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