François Major

The paper develops a hypoxia-mimicking culture protocol and identifies a novel WT1 isoform linked to poor ovarian cancer survival.

The paper introduces a numerical method to rapidly generate RNA ribose conformations with high accuracy, improving modeling efficiency and identifying irregular stereoisomers in RNA structures.

The paper evaluates how machine learning models handle RNA secondary structure prediction and complementary strand recognition, finding that model capacity and data quality significantly impact performance.

The paper introduces CAMAP, an artificial neural network that predicts MHC-I peptide presentation by analyzing codon arrangement in mRNA sequences, improving prediction accuracy beyond traditional methods.

The paper adapts the Elastic Network Contact Model (ENCoM) for RNA dynamics, showing improved accuracy in predicting conformational changes and structural variance compared to existing methods.