Xiaoting Li

The paper integrates CSF and EV proteomes to enhance biomarker discovery by capturing shared variation across compartments, improving biological concordance and identifying more candidates in a smaller cohort.

The paper characterizes the miRNome of African green monkey cerebrospinal fluid, identifying conserved miRNAs relevant to human neurodegenerative diseases and highlighting species-specific variations.

The paper develops a sequence-to-affinity model for SH2 domains using multi-round peptide binding assays and free-energy regression to predict binding specificity and variant effects.

The paper benchmarks DNA binding affinity models using allele-specific ChIP-seq data to predict allelic imbalances and improve TF binding models.

The paper identifies genetic variants affecting transcription factor activity by analyzing RNA-seq data from GTEx and using a beta-binomial GLM to map aQTLs.

The paper introduces ProBound, a machine learning framework that quantifies biophysical parameters of protein-ligand interactions using NGS data, and couples it with Kd-seq to determine absolute affinities.