TITLE: "Asymmetric Branching Scale Factors as Features in Neuronal Cell-Type Classification" ABSTRACT: Neurons are connected by complex branching processes - axons and dendrites - that process information for organisms to respond to their environment. Classifying neurons according to differences in structure or function is a fundamental piece of neuroscience. In previous work, we constructed a […]

“The role of chromosome evolution in adaptation and speciation in stickleback fish” Hosted by Kirk Lohmueller

TITLE: “TBD” Hosted by Sarah Spendlove

TITLE: "Comprehensive study of gene expression outliers and their regulation mechanisms in pan-cancer." ABSTRACT: With the ultimate aim of improving the management of cancer, many groups have studied the molecular characteristics of tumors. Typically, the heterogeneity of cancer evolves rapidly to adapt to its microenvironment, helping cancer evade selective pressures and progress. These various subclones […]

TITLE: "Massive parallelization of massive sample-size survival analysis." ABSTRACT: Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of […]

TITLE: "Climate change and maize response to pollutants: gene content, expression and regulation." Hosted by Kirk Lohmueller.

TITLE: "Phenotypic consequences of gene expression variability." ABSTRACT: The central dogma of biology proposes that information flows from genes to RNA to protein, determining protein identity and abundance. Single cell transcriptome measurements provide ample information about RNA abundance in cells. While the expression of individual genes clearly matters for determining phenotype, they do not work […]

TITLE: "scDesign3: an all-in-one statistical framework that generates realistic single-cell omics data and infers cell heterogeneity structure." ABSTRACT: The generation of realistic synthetic data is essential for benchmarking numerous computation tools developed for single-cell omics data. Here we propose an all-in-one statistical framework that generates single-cell omics data from various cell heterogeneity structures, including discrete cell […]

TITLE: "Deriving network parameters for understanding drug effects.” Hosted by Jason Ernst.