bayesReact

BayesReact (BAYESian modeling of Regular Expression ACTivity) is a fully Bayesian and unsupervised generative model, which enables miRNA and other motif-mediated regulatory activity inference from sparse single-cell gene expression data. (2026).

LIONHEART

A method for detecting cancer from whole genome sequenced plasma cell-free DNA (2025)

CASTLE

R package for Circulating Tumor DNA Detection by Droplet Digital PCR (2022)

DREAMS

Deep Read-level Error Model for Sequencing data. Includes modules for ctDNA variant calling and cancer detection (2022)

GENIUS

Multiomics data analysis based on spatial transformation (2022)

KMERPAPA

Bioinformatic tool to calculate a "k-mer pattern partition" from position specific k-mer counts (2021)

NMIBC CLASSIFIER

An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer (2021)

MIREACT

miRNA reactivities inferred from single cell RNAseq expression data (2021)

CSG PREDICTION

Classifying cGAS-STING activity links chromosomal instability with immunotherapy response in metastatic bladder cancer (2021)

GENOVO

Identifying disease genes with de-novo mutations (2020)

METHCORR SOFTWARE

Infer iRNA expression scores for 11 cancer types from DNA methylation 450K chip β-values (2020)

REGMEX

Regmex is a package for evaluation of motif rank correlation in a list of ranked DNA or RNA sequences (2018)

NORMFINDER

NormFinder is an algorithm for identifying the optimal normalization gene among a set of candidates (2004)

Person with gray hair viewing data analysis and code displayed on an Apple iMac screen.
NORMFINDER

NormFinder is an algorithm for identifying the optimal normalization gene among a set of candidates (2004)

Scatter plot showing clustered data points on PC1 and PC2 axes with different colors and shapes representing distinct groups.
methcorr software

Infer iRNA expression scores for 11 cancer types from DNA methylation 450K chip β-values (2020)

Heatmap showing genomic and clinical data with annotations including genome altered, ploidy, tumor stage, tumor grade, risk scores, progression signatures, transcriptomic class, T1HG subtypes, and immune scores, accompanied by pie charts summarizing stage, grade, tumor cell, and immune score distributions.
NMIBC Classifier

An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer (2021)

Bar graph comparing false positive rates for different methods including CASTLE, Poisson, ALPACA, Dynamic LOB, Static LOB, M2, M2S1, M3, and M5, showing lowest rates for CASTLE and Poisson.
CASTLE

R package for Circulating Tumor DNA Detection by Droplet Digital PCR (2022)

Color-coded cell type map showing various cell clusters including epithelial, endothelial, fibroblasts, immune cells like B cells, T cells, and microglia, with organ-specific labels in pastel colors.
miReact

miRNA reactivities inferred from single cell RNAseq expression data (2021)

Line graph showing the increase in -log(RCP) with the number of inserted motifs, comparing no insertion and multiple inserted motifs combined with logical OR, with lines for m1, m1|m2, m1|m2|m3, and m1|m2|m3|m4.
RegMex

Regmex is a package for evaluation of motif rank correlation in a list of ranked DNA or RNA sequences (2018)

Side-by-side portrait photos of two men with glasses, one with a beard wearing a brown shirt and the other clean-shaven wearing a red checkered shirt.
DREAMs

Deep Read-level Error Model for Sequencing data. Includes modules for ctDNA variant calling and cancer detection (2022)

Smiling man with glasses and short hair wearing a light blue shirt against a solid brown background.
CSG prediction

Classifying cGAS-STING activity links chromosomal instability with immunotherapy response in metastatic bladder cancer (2021)

Smiling man with glasses wearing a blue shirt and dark blazer outdoors with blurred green background.
kmerpapa

Bioinformatic tool to calculate a "k-mer pattern partition" from position specific k-mer counts (2021)

Flowchart diagram illustrating a pipeline starting with GFF3 annotations and observed mutations, leading through transcript-only annotations to classification, expectation, and comparison of mutations using kmerPaPa p-values and statistics.
GENOVO

Identifying disease genes with de-novo mutations (2020)