Jianguo Zhu

IIIandMe improves haplotype phasing accuracy using three gametes, validated through simulation and citrus data.

This study evaluates 15 machine learning algorithms and 30 gene expression signatures for prostate cancer prognosis using 1,558 patients, finding survival analysis models outperform binary classifiers and identifying top signatures comparable to commercial panels.

CancerMIRNome provides an interactive database for analyzing and visualizing miRNA expression data from cancer studies to identify dysregulated miRNAs and develop biomarkers.

PCaDB provides a database for analyzing transcriptomes from prostate cancer cohorts and includes scRNA-seq data and prognostic signatures to aid in diagnosis and prognosis.

The paper applies genomic selection methods to multi-omics data to develop prognostic signatures for prostate cancer, showing improved predictability and computational efficiency.

Hapi infers chromosome-length haplotypes from 3-5 gametes, outperforming existing methods in accuracy, reliability, and cost efficiency.

GDCRNATools provides an R/Bioconductor package for analyzing lncRNA, miRNA, and mRNA data from the GDC to study ceRNA regulatory networks in cancers.