Update README.md

README.md

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 **FusOn-pLM: A Fusion Oncoprotein-Specific Language Model via Focused Probabilistic Masking**
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64cd5b3f0494187a9e8b7c69/eR38p4VJhWJhwsqjZZdYp.png)
-In this work, we introduce FusOn-pLM, a novel pLM that fine-tunes state-of-the-art ESM-2 embeddings on fusion oncoprotein sequences, those that drive a large portion of pediatric cancers but are heavily disordered and undruggable, via masked language modeling (MLM). We specifically introduce a novel MLM strategy, employing a binding-site probability predictor to focus masking on key amino acid residues, thereby generating more optimal fusion oncoprotein-aware embeddings. Our model improves performance on both fusion oncoprotein-specific benchmarks and disorder prediction tasks in comparison to baseline ESM-2 representations, as well as manually-constructed biophysical embeddings, motivating downstream usage of FusOn-pLM embeddings for therapeutic design tasks targeting these fusions.
+In this work, we introduce **FusOn-pLM**, a novel pLM that fine-tunes state-of-the-art ESM-2 embeddings on fusion oncoprotein sequences, those that drive a large portion of pediatric cancers but are heavily disordered and undruggable, via masked language modeling (MLM). We specifically introduce a novel MLM strategy, employing a binding-site probability predictor to focus masking on key amino acid residues, thereby generating more optimal fusion oncoprotein-aware embeddings. Our model improves performance on both fusion oncoprotein-specific benchmarks and disorder prediction tasks in comparison to baseline ESM-2 representations, as well as manually-constructed biophysical embeddings, motivating downstream usage of FusOn-pLM embeddings for therapeutic design tasks targeting these fusions.
 
 
 # How to Use FusOn-pLM