Training and evaluating a variational autoencoder for pan-cancer gene expression data

What you need to process the Quarterly DepMap-Omics releases from Terra

Identifying tumor cells at the single-cell level using machine learning

Backend Server for CIViC Project

Pan-cancer quantification of neoantigen-mediated immunoediting in cancer evolution

Analysis for "K27M in canonical and noncanonical H3 variants occurs in distinct oligodendroglial cell lineages in brain midline gliomas" (Jessa et al, Nature Genetics, 2022)

To use deep learning to identify patients whose tumor DNA mutations “look similar to” other tumors for which treatments are effective.

Deterministic evolution and stringent selection during pre-neoplasia

ImaGene: A multi-omic ML/AI software with guided operational reports and supporting files

Binomial and Beta-Binomial mixture models for counts data.

Neoantigen Discovery pipeline

Applying Machine Learning Ras, NF1, and TP53 Classifiers to PDX model gene expression

The evoverse is a package to implement cancer evolution analysis on multi-sample cancer sequecing data.

Lab website.

Learn to analyse genomics data from cancer samples with hands-on practical exercises in mutation calling, driver gene identification, mutational signature analysis and RNA deconvolution.

Learn to analyse genomics data from cancer samples with hands-on practical exercises in mutation calling, driver gene identification, mutational signature analysis and RNA deconvolution.

Analysis of paired tumor-normal whole exome sequencing data generated in a pilot open-access study of participants in Texas.

Cell-of-origin analysis for "Histone H3.3G34-mutant interneuron progenitors co-opt PDGFRA for gliomagenesis" (Chen*, Deshmukh*, Jessa*, Hadjadj*, et al, Cell, 2020)