Marco Mesiti

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.

parSMURF is a parallel machine learning tool for detecting pathogenic variants in genomic data, achieving state-of-the-art results with an 80x speed-up over sequential versions.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.