Julie Hussin

This paper shows PHATE can better capture continuous population structure in genomic data than PCA or UMAP by tuning scale and balancing populations.

This paper develops a deep learning framework for genomic ancestry prediction that is generalizable across diverse populations and interpretable using attribution techniques.

The paper models HLA diversity and viral evolution to assess T cell escape risk across populations, finding that escape mutations gain fitness with additional mutations.

This study shows that CETP isoforms influence human traits, particularly in pituitary and thyroid glands, and reveals tissue-specific CVD associations and epistatic interactions involving ADCY9.

The paper constructs a genetic reference panel for the Quebec population using whole-genome sequencing and GWAS to improve phenotype-genotype association discovery.

The paper identifies cell-type-specific enhancer-gene interactions in cardiac cells and maps disease-associated SNPs to enhancers in endothelial cells.

The paper identifies LIPTER, a cardiomyocyte-specific long noncoding RNA, and shows it regulates cytoskeletal maturation and is controlled by a cardiomyocyte-specific enhancer.

The paper identifies enriched coding variants in a French-Canadian founder population and explores their association with inflammatory bowel diseases.

This paper develops a multi-Polygenic Risk Score (multiPRS) to predict diabetes complications in different ethnic groups, finding that logistic regression with Mondrian Cross-Conformal Prediction improves prediction accuracy in non-European populations.

This study uses machine learning and explainable AI to identify metabolite markers for heart failure, finding that lignoceric acid is a significant discriminator.

The paper develops a workflow to improve the detection of intra-host SARS-CoV-2 mutations by reducing sequencing artifacts and batch effects using large-scale NGS data.

This study examines how temperature affects Spike-ACE2 interactions in Omicron subvariants and links these interactions to viral transmission efficiency.

This study identifies co-evolution and co-regulation in CYP3A and CYP4F genes across human populations, linking genetic diversity to drug metabolism and health outcomes.

The paper maps genetic variation's effect on protein-protein interactions in yeast, finding trans-acting SNPs have larger effects than cis-acting ones and SNPs in non-coding regions are significant.

This paper introduces the shortest reactional distance (SRD) metric to improve the biological interpretation of metabolite genome-wide association study (mGWAS) results by linking genes to metabolites based on metabolic pathway knowledge.

This study examines how sex and BMI modify the effect of genetically predicted lower CETP levels on lipid biomarkers and cardiovascular outcomes using observational genetic data.

The paper computes local feature attributions for t-SNE using gradient-based methods and validates them on synthetic and biological datasets.

This study shows that immunosuppressed patients with hematologic cancers develop viral populations with immune escape mutations, suggesting the need for combination therapies.

The paper identifies immune evasion mechanisms in SARS-CoV-2 by mapping epitopes and analyzing viral genome sequences to understand evolutionary pressures and immune responses.

The paper compares protein aggregation propensity and mutation tolerance between naked mole-rat and mouse orthologous proteins, finding significant differences in specific protein subsets related to aging processes.

The paper identifies a sex-specific evolutionary interaction between ADCY9 and CETP variants associated with dalcetrapib response and cardiovascular traits.

The paper presents computational tools for analyzing SARS-CoV-2 genetic data, showing that haplotype networks and unsupervised learning can reveal lineage emergence and population dynamics.

ImputeCoVNet is a 2D ResNet Autoencoder for imputing missing SARS-CoV-2 variants, achieving accurate results at low allele frequencies with faster computation than Hamming distance methods.

This paper develops a polygenic risk score model to predict diabetic complications and their response to therapy using genome-wide association study data.

The paper uses single-cell RNA-sequencing to show genetic ancestry affects gene expression during viral infection, with effects varying by cell type.

The paper introduces Multiscale PHATE, a method for exploring high-dimensional biomedical data, and applies it to identify immune cell signatures associated with severe COVID-19 outcomes.

This paper compares three polygenic risk scores for coronary artery disease and identifies issues with their clinical use due to population structure confounding and missing data effects.

This study uses whole-genome sequencing of platypuses to reveal strong population structure, recent bottlenecks, and genetic relationships across their range.

The paper shows that recombination rate influences the distribution of deleterious variants across the human genome, with low recombination regions enriched for disease-associated mutations.