Keith A. Boroevich

vDeepInsight introduces a 3D voxel carrier for tabular data that improves feature neighborhood representation, leading to better performance in gene expression tasks.

This paper develops a dynamic multimodal model for survival prediction in multiple myeloma using gene expression, lab data, and treatment history, achieving higher accuracy than existing methods.

scHDeepInsight improves immune cell classification in scRNA-seq by using a hierarchical deep learning framework with adaptive loss and image-based features, achieving 93.2% accuracy across seven datasets.

VBayesMM improves identification of microbial-metabolite relationships in high-dimensional microbiome data by using a Bayesian neural network with spike-and-slab priors.

The paper introduces MRep-DeepInsight, a method that improves tabular data analysis by using multiple representations, showing better accuracy than traditional methods on various datasets.

scDeepInsight improves cell-type annotation in scRNA-seq by using supervised deep learning, achieving higher accuracy than existing methods.

DeepInsight-3D predicts patient-specific anticancer drug response using multi-omics data and convolutional neural networks, outperforming existing methods.

DeepInsight-FS uses a convolutional neural network to select clinically relevant gene subsets from non-image data by converting samples into image-form.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

This paper develops a machine learning method to predict drug toxicity by integrating drug targets, functional impact scores, and biological networks, showing it can identify toxic drugs and support safe combination design.

The paper uses empirical Bayes estimation of semi-parametric hierarchical mixture models to analyze GWAS summary statistics and characterize the polygenic architectures of schizophrenia and rheumatoid arthritis.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.