Kevin Yuk-Lap Yip

This study identifies age-associated repression of HNF4α as a driver of liver metabolic dysfunction and shows that restoring HNF4α can reverse some age-related liver defects.

The paper analyzes spatial relationships between AD pathology and gene expression in human hippocampus using MERFISH and snRNA-seq data, identifying cell-type and genotype-specific effects of A{beta} and tau pathology.

This paper introduces a measure of transcriptional dyscoordination to quantify aging in single cells, showing it increases with age and is linked to cellular processes like senescence and genome instability.

This paper introduces SHARP, a method to enhance Hi-C data resolution while avoiding artificial structures by decomposing signals and using multi-scale attention.

This study shows that targeting CCND1-CDK6 reduces DNA damage, inflammation, and age-related decline in mice by suppressing SASP and ISG expression.

This study shows that aging-induced CD44 in hepatocytes activates the IL6/JAK/STAT3 pathway, leading to impaired T cell function in the liver.

This study evaluates how large language models perform in-class learning for antibody classification using biological sequences, finding that few-shot prompting with diverse demonstrations achieves competitive results.

The paper identifies a widespread transcript leakage issue in spatial transcriptomics and introduces DeLeakage, a Bayesian method that improves data cleaning and cell-type annotation.

This study shows that HIRA-mediated H3.3 deposition maintains hepatocyte identity during aging, and its loss leads to metabolic dysfunction and fibrosis, which can be partially reversed by tissue regeneration.

This paper introduces a framework that guides dimensionality reduction using knowledge-derived genes to recover rare but important signals in single-cell data, demonstrating its effectiveness in identifying rare cell subtypes.

The paper introduces PathStAR, a framework to quantify structural aging from histopathology images, revealing tissue-specific aging trajectories and coordinated deterioration across organs.

The paper models transcript isoform expression levels using epigenomic data from 324 human samples, showing that a universal model outperforms tissue-specific models.

The paper investigates how three-dimensional chromatin domain structures influence gene regulation and finds that domain surfaces are enriched for regulatory elements and disease variants.

This study shows that increasing SORLA levels in aged tauopathy mice reduces multiple pathological effects, including tau phosphorylation, inflammation, and synaptic dysfunction.

This study shows that inactivating HIRA during embryonic development affects melanoblasts and leads to aging-related defects in melanocyte stem cells, but not in postnatal melanocytes.

ecPath detects ecDNA in tumors using H&E images, achieving good performance in predicting ecDNA status and identifying poor prognoses in low grade glioma patients.

This study identifies epigenetic changes in HCC that affect cis-regulatory elements and retrotransposons, linking them to gene dysregulation and poor prognosis.

The paper evaluates 10 software tools for computing AUPRC and finds they produce conflicting and overly-optimistic results.

This paper presents a chromosomal-level genome assembly of the long-spined sea urchin D. setosum using PacBio and Omni-C technologies, providing a resource for ecological and evolutionary studies.

This study presents the genome assembly of the edible jelly fungus Dacryopinax spathularia using PacBio HiFi and Omni-C data, revealing a high-quality genome with 11,510 protein-coding genes.

The paper introduces COMSV, a method for identifying structural variations in cancer samples, showing high sensitivity and specificity and detecting hundreds of novel SVs per sample.

The paper introduces a linked-pair long-read sequencing strategy for targeted resequencing, enabling efficient enrichment of genomic regions with haplotype information.

The paper evaluates 23 imputation models for cross-cell type epigenomic data, finding quality assessment challenging due to distributional shifts, data availability, and redundant metrics.

This paper introduces RHSNet, a deep learning method that identifies and quantifies recombination hotspot determinants across species, sexes, and populations.

The paper introduces psirc, a method to identify and quantify full-length circRNA isoforms from RNA sequencing data, showing its effectiveness on cancer and normal tissue samples.

The paper identifies noncoding genetic variants associated with Hirschsprung disease using a framework that captures multi-granular regulatory element perturbations.

The paper analyzes transcriptome and methylome data from mouse oocytes to understand aging and maturation, identifying biomarkers and a predictive model for oocyte quality.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

This paper develops a cross-organism framework for enhancer prediction using Drosophila STARR-seq data and supervised machine learning, validated in mouse and human with 153 enhancers.

CQF-deNoise reduces memory usage for k-mer counting in single-cell RNA-seq, enabling faster clustering with accuracy comparable to CellRanger.