Mengyi Sun

This paper shows that AI, specifically AlphaFold2, redirected scientific attention toward novel research targets in structural biology, increasing engagement with understudied genes and proteins.

This study introduces pVEP, a framework showing how genetic background affects AI predictions of variant effects, revealing variant effects vary across haplotypes.

The paper identifies genetic determinants of gene expression noise and links them to complex trait variation using a large-scale human blood cell dataset.

The paper finds that positive genetic interactions have greater scientific impact but are underrepresented in literature, despite equal detection capability.

The paper characterizes PaAOX1, a plant AA3 oxidase from Norway spruce, revealing its unique biochemical activity and structural features within the GMC oxidoreductase superfamily.

The paper analyzes over 350,000 amino acid replacements to show that fitness effects follow a maximum entropy distribution, with implications for mutational prediction and protein design.

The paper analyzes how research attention on disease-target relationships predicts clinical trial success using Open Targets data.

The paper provides proteomic evidence supporting the translational accuracy hypothesis of codon usage bias across over 1000 taxa, showing codon-specific translational accuracies correlate with tRNA abundance.

This study uses allele-specific single-cell RNA sequencing to estimate intrinsic and extrinsic gene expression noise in mouse fibroblasts and identifies factors influencing these noises.

The paper shows that gene expression co-fluctuates across distant genes on the same chromosome due to chromatin dynamics and gene linkage.