Neoantigen Discovery pipeline
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Updated
Oct 18, 2018 - HTML
Neoantigen Discovery pipeline
To use deep learning to identify patients whose tumor DNA mutations “look similar to” other tumors for which treatments are effective.
Training and evaluating a variational autoencoder for pan-cancer gene expression data
Applying Machine Learning Ras, NF1, and TP53 Classifiers to PDX model gene expression
Pan-cancer quantification of neoantigen-mediated immunoediting in cancer evolution
Binomial and Beta-Binomial mixture models for counts data.
Cell-of-origin analysis for "Histone H3.3G34-mutant interneuron progenitors co-opt PDGFRA for gliomagenesis" (Chen*, Deshmukh*, Jessa*, Hadjadj*, et al, Cell, 2020)
The evoverse is a package to implement cancer evolution analysis on multi-sample cancer sequecing data.
Deterministic evolution and stringent selection during pre-neoplasia
Backend Server for CIViC Project
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)
Analysis of paired tumor-normal whole exome sequencing data generated in a pilot open-access study of participants in Texas.
ImaGene: A multi-omic ML/AI software with guided operational reports and supporting files
Lab website.
Identifying tumor cells at the single-cell level using machine learning
What you need to process the Quarterly DepMap-Omics releases from Terra
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