Sikander Hayat

This study uses Deep Visual Proteomics to analyze VSMC phenotypes in atherosclerotic plaques, finding spatially distinct phenotype distributions and a shift toward dedifferentiated states in unstable plaques.

The paper introduces scVizComm, a web tool for visualizing cell-cell communication in single-cell transcriptomics data with pathway-centric prioritization.

SwarmMAP uses swarm learning to automate cell-type annotation in single-cell data without sharing raw data, achieving high accuracy across human tissues.

This study identifies polyploid cardiomyocytes as disease-specific transcriptional states in the mammalian heart and suggests targeting them for heart failure treatment.

Single-cell and spatial maps of human coronary arteries identify FAP as a marker of SMC-derived modulated cells; an anti-FAP BiTE reduced plaque burden in mouse models.

This study uses multimodal single-cell profiling to identify immune cell crosstalk mechanisms in poor prognostic cholangiocarcinoma, revealing key ligand pairs and pathways contributing to immune exhaustion.

This paper identifies immunomodulatory endothelial cells (IMEC) that present antigens on MHC class II and activate T cells in atherosclerosis.

The paper evaluates structure-based methods to predict how kinase mutations affect inhibitor binding, finding they accurately classify mutations as resistant or sensitizing.

mcBERT generates patient-level representations from scRNA-seq data using a transformer-based model, enabling disease cohort identification and phenotype classification across diverse tissues.

This study investigates RFX3's role in human pancreatic islet development using iPSC-derived organoids, finding that RFX3 loss disrupts endocrine cell differentiation and beta-cell function.

PICASO integrates single-cell gene expression data with biomedical databases to create networks that compare disease states and identify druggable targets.

scDecorr provides a self-supervised framework for integrating single-cell RNA-seq data by decorrelating features and adapting to domain differences, improving clustering and label transfer.

This study shows that RFX6 deletion in iPSCs impairs islet organoid development and survival through apoptosis and gene downregulation, without affecting PDX1+/NKX6.1+ progenitors.

The paper integrates single-cell RNA-seq and spatial transcriptomics data to reveal cellular interactions and changes in human atherosclerosis.

MASIv2 provides a framework for standardizing and integrating multi-modal single-cell and spatial omics data through shared and specific features.

Minuteman provides a secure cloud platform for collaborative biomedical data analysis with features for data management, access control, and interactive exploration.

This study generates a high-resolution spatial multi-omic map of human myocardial infarction by integrating single-cell gene expression, chromatin accessibility, and spatial transcriptomic data to reveal cellular zones of injury, repair, and remodeling.

MASI provides a fast, model-free method for standardizing cell-type annotation and integrating single-cell transcriptomics datasets, achieving comparable or superior performance to existing methods.

SciViewer is an interactive tool for visualizing and analyzing single-cell datasets, offering target-centric views and integration with pharmacogenomics databases.

MACA is a tool for automatic cell-type annotation in single-cell transcriptomics datasets, showing improved accuracy and speed compared to existing methods.

The paper identifies four molecular clusters in systemic autoimmune diseases using transcriptome and methylome data, showing three stable aberrant patterns and one non-specific cluster.

This paper develops a deep learning method to classify colorectal cancer subtypes and match them to cell lines using multi-omics data.