Kevin Lin

This study quantifies cancer stemness in prostate cancer across progression stages, showing its association with therapy resistance, poor survival, and lineage plasticity.

GeoAdvAE integrates unpaired single-cell morphology and transcriptomics to align cell types and reveal disease-associated microglia signatures.

This paper introduces sensGAN, a deep-learning framework to identify worst-case latent confounders in genomics, improving sensitivity analysis for disease-associated gene associations.

This study validates BCL-2 as a therapeutic target in CRPC by showing its upregulation under AR inhibition and demonstrating efficacy of BCL-2 inhibitors in preclinical and clinical models.

SCOPE identifies fate-decision states and regulatory drivers in single-cell differentiation by localizing branchpoints and epigenetic priming across developmental processes.

The paper introduces a framework to assess RNA velocity stability using count splitting and replicate coherence, showing performance differences across methods and guiding model selection.

Clonotrace integrates gene expression and clonotype data to infer cell state transitions and fate biases, improving understanding of lineage dynamics in various biological systems.

This study identifies canonical AREs as tumor suppressive regulatory elements in prostate cancer, showing their reactivation can induce death in AR+ cancer cells and is linked to better prognosis.

This study introduces ODEinherit, a framework using ODEs to quantify regulatory network inheritance in yeast cells, revealing heterogeneity in inheritance rates across mother-daughter pairs.

This paper introduces LCL, a contrastive learning framework using lineage barcodes to isolate fate-determining signals in scRNA-seq data, showing improved performance over standard models.

The paper identifies ADA2B's role in maintaining oncogenic gene expression programs in multiple myeloma through epigenetic mechanisms.

eSVD-DE identifies differentially expressed genes across large cohorts of single-cell RNA-seq data by reducing dimensionality and accounting for individual-level confounding factors.

Destin2 improves cross-modality analysis of scATAC-seq data by integrating multiple epigenomic features for dimension reduction, clustering, and trajectory inference.

The paper introduces MaxFuse, a method for integrating spatial and single-cell omics data with weak linkage, demonstrating its effectiveness in consolidating proteomic, transcriptomic, and epigenomic information.

This study compares four ChIP-Seq library preparation protocols for three different protein targets, finding that each protocol performs best for specific enrichment patterns and input DNA levels.

This paper introduces Tilted-CCA, a method to quantify common and distinct information in multimodal single-cell data, demonstrating its application in antibody panel design and multiome analysis.

This paper introduces eSVD, a non-linear embedding method for single-cell RNA-seq data, and applies it to identify two distinct developmental trajectories in mouse oligodendrocytes.