Harpreet Kaur

The paper analyzes transcriptomic data from asymptomatic and severe COVID-19 patients to identify molecular differences and associated comorbidities.

The paper introduces a software package that computes over 50,000 features for protein/peptide function prediction, including residue-level annotations.

The paper analyzes gene expression profiles from RTS,S malaria vaccine doses to assess efficacy and identify cytokine responses associated with protection.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper examines the impact of simulated microgravity on DNA damage response in zebrafish embryos and compares it to human spaceflight effects observed in the NASA twin study.

This study analyzes transcriptomic data from Multiple Sclerosis patients to identify differentially expressed genes and their association with neurological and developmental pathways.

SAPdb is a database of experimentally validated peptide nanostructures formed by self-assembly, providing information on chemical modifications, nanostructure types, and experimental conditions.

The paper develops a prognostic model using multi-omics data to stratify HCC patients into recurrence risk groups, identifying key RNA, miRNA, and methylation features with significant predictive performance.

This study identifies key transcripts and machine learning models to classify early and late stage Papillary Thyroid Carcinoma samples using TCGA data, achieving up to 75.43% accuracy.

The paper develops prediction models using HLA-alleles and clinical characteristics to predict overall survival in skin cutaneous melanoma patients and identifies favorable and unfavorable superalleles.

This study identifies a three-gene biomarker panel for hepatocellular carcinoma using large-scale transcriptomics data, showing high accuracy and prognostic potential across multiple platforms.

GPSRdocker provides a docker-based container integrating bioinformatics tools for genomics, proteomics, and systems biology, enabling local or cloud use of computational resources.

This study predicts melanoma progression using genomic features from TCGA SKCM data, identifying key genes and miRNAs that distinguish primary from metastatic tumors.

The paper develops models to classify early and late stage Liver Hepatocellular Carcinoma using genomic and epigenomic data, achieving high accuracy and AUROC scores.