Valentin Dinu

The paper identifies multiple genetic variants associated with childhood apraxia of speech through whole-exome sequencing and functional analysis, proposing four novel candidate genes.

This study analyzes exome and metabolome data from individuals with dyslexia to identify associated dysregulations in olfactory perception and immune functions.

This study uses data fusion and machine learning to analyze multi-omics and clinical data, finding that ensemble methods outperform traditional techniques in identifying developmental dyslexia with high accuracy.

The paper analyzes mRNA data from 17 cancer types to identify dysregulated pathways, finding Pathways in Cancer as the most common and MAPK signaling as the most influential.