Faraz Faghri

The paper identifies CypA as a molecular barrier preventing lentivirus from Eastern chimpanzees from infecting humans, due to its restriction of replication through a unique capsid feature.

CARDBiomedBench evaluates LLM performance in biomedical research using a benchmark focused on neurodegenerative diseases, revealing significant limitations in response quality and safety.

The paper identifies BORC as a modifier of TDP-43 protein levels in neurons, showing lysosome transport is required for TDP-43 turnover.

GenoTools provides a Python package for genotype data QC, ancestry estimation, and GWAS, enabling scalable and reproducible analysis of large genetic datasets.

The paper introduces the NeuroBooster Array, a genome-wide genotyping platform designed to study neurological disorders across diverse populations by capturing genetic variation and enabling imputation of disease-related variants.

This study evaluates federated learning models for multi-omics Parkinson's disease prediction, finding they perform comparably to centrally trained models with minor performance differences.

The paper identifies druggable target genes for neurodegenerative diseases using Mendelian Randomization and provides a web platform for exploring therapeutic targets.

This study clusters patients with neurodegenerative diseases based on genetic risk variants, revealing overlapping genetic etiology and subsets with no common risk factors.

This study uses machine learning to identify Parkinson's disease subtypes and predict progression in two cohorts, finding three subtypes with distinct progression rates and high prediction accuracy.

The paper identifies and characterizes a reference iPSC line, KOLF2.1J, for standardized use in collaborative studies across multiple differentiation protocols and assays.

This paper develops an automated ML model to predict Parkinson's Disease using multi-modal data, achieving high AUC scores and constructing gene networks relevant to PD.

This paper applies Aligned-UMAP to longitudinal biomedical datasets to reveal time-dependent patterns and provides best practices for its use.

The paper presents a fully automated FAIMS-DIA proteomic pipeline for iPSC-derived neurons, showing high protein identification and reproducibility, validated against single-cell RNA-Seq data.

PhenoExam is an R package and web tool that performs phenotype and disease enrichment analysis on gene sets, detecting significant phenotypic similarities and differences across databases.

This study uses a CRISPRi/a platform in iPSC-derived microglia to identify genes regulating microglia states and their therapeutic targets.

The paper performs genome-wide CRISPRi/a screens in human neurons to identify gene functions related to oxidative stress and neurodegenerative diseases, finding that lysosomal failure links to ferroptosis.

The paper identifies human-lineage-specific genomic elements, called CNCRs, and shows their enrichment in neurological disease genes and APOE intron-3 retention transcripts in Alzheimer's disease.

The paper presents a GWAS locus browser for Parkinson's disease that helps prioritize genes associated with risk variants from multiple GWAS studies.

This paper uses machine learning to predict Alzheimer's disease progression rates and classify clinical subtypes based on early clinical data.

The paper identifies de-novo SPTLC1 mutations in juvenile ALS patients and suggests serine supplementation may alleviate symptoms.

The paper identifies pathway-specific polygenic risk factors and transcriptomic networks associated with Parkinson disease using large-scale genetic and expression data.

The paper identifies disease-relevant regulatory loci in human basal ganglia using genomic and transcriptomic data from 117 brains, highlighting splicing eQTLs and ASE analyses as sources of cell-specific regulatory information.

This study shows that a polygenic risk score increases the likelihood of Parkinson's disease in LRRK2 p.G2019S carriers, with stronger effects in younger individuals.

This study simulates clinical trials to show genetic differences between trial arms can lead to false negatives and positives, especially in small samples, and suggests genetic balancing improves outcomes.

Across 37,688 Parkinson's cases, 18,618 proxy-cases and 1.4M controls, 90 risk signals map to 78 loci (37 new); an 1805-variant polygenic score explains 26-36% of heritability.

This paper uses LSTM-based RNNs to predict ICU readmissions from EHR data, achieving higher sensitivity and AUC than traditional methods.

This study connects Parkinson’s disease heritability to cell-type-specific gene regulation, finding enrichment in lysosomal genes in astrocytic and microglial subtypes rather than specific cell types or brain regions.

This study identifies two genome-wide significant variants associated with Parkinson's disease progression and replicates known risk variants' effects on motor and cognitive decline.

This study uses machine learning to identify Parkinson disease subtypes and predict progression, finding three distinct subtypes with accurate progression predictions.