Emanuele Cavalleri

RNAcentral integrates literature and gene-level data to enhance non-coding RNA sequence analysis and functional understanding.

The paper analyzes RNA-KG using homogeneous graph embedding methods to predict node types and edges, achieving high accuracy in classification and edge prediction tasks.

This paper introduces a method to guide dimensionality reduction and data fusion in multi-omics data by analyzing intrinsic dimensionality, showing improved performance in predicting survival outcomes.

This paper introduces Finenzyme, a Protein Language Model that improves EC category prediction through fine-tuning, generating functionally similar enzymes with preserved structure and function.