Ruidong Li

Machine learning-based definition of cellular senescence reveals pro-senescence potential implication in lung adenocarcinoma

This study introduces PreCSenM, a machine learning model that quantifies cellular senescence and identifies HDAC inhibitors as senescence inducers in lung adenocarcinoma.

IIIandMe: An Algorithm for Chromosome-scale Haplotype Determination Using Genome-wide Variants of Three Haploid Reproductive Cells

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

Comprehensive evaluation of machine learning models and gene expression signatures for prostate cancer prognosis using large population cohorts

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: an interactive analysis and visualization database for miRNome profiles of human cancer

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

PCaDB - a comprehensive and interactive database for transcriptomes from prostate cancer population cohorts

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.

Extended Application of Genomic Selection to Screen Multi-Omics Data for Prognostic Signatures of Prostate Cancer

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

Inference of Chromosome-length Haplotypes Using Genomic Data of Three to Five Single Gametes

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

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA, and mRNA data in GDC

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

Optimizing Trait Predictability in Hybrid Rice Using Superior Prediction Models and Selective Omic Datasets

The paper evaluates prediction strategies for hybrid rice traits using various omic datasets and methods, finding genomic-metabolomic combinations and BLUP as most effective.