Justin Guinney

The paper describes how the Human Tumor Atlas Network shares data through standards, infrastructure, and community engagement, making datasets accessible via multiple platforms.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

ImmClassifier improves fine-grained immune cell type classification in scRNA-Seq data with higher recall and precision than existing tools.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper uses statistical and machine learning methods to analyze NF1 tumor genomic data, identifying key signaling pathways and molecular features associated with different tumor types and potential drug targets.

The paper benchmarks somatic structural variant detection methods using synthetic tumors and crowd-sourced submissions, finding that ensembles do not always outperform individual methods.

This paper presents a crowdsourced challenge to predict early discontinuation of docetaxel in mCRPC patients using clinical data from multiple trials, finding that ensemble models outperform individual models.

The paper quantifies proteomic and genomic variation in colorectal cancer cell lines to understand how mutations affect protein levels and drug responses.