Events for November 13, 2023 - February 6, 2025

TITLE: “Pushing the boundaries of causal inference.”

TITLE: "“Cross-tissue multiomics studies reveal gutbrain interactions mediating the effect of Akkermansia muciniphila in counteracting fructose-induced obesity” ABSTRACT: The gut bacterium Akkermansia muciniphila (A. muciniphila) has been implicated in anti-obesity effects, but a systems level understanding of the molecular mechanisms is lacking. We carried out multiomics studies to investigate the molecular cascades mediating the anti-obesity effect of A. muciniphila in a fructoseinduced obesity mouse model. […]

TITLE: "Exercise and tumor genomic landscapes in 2,702 patients with cancer" ABSTRACT: Approximately two-thirds of cancer diagnoses globally are attributed to modifiable lifestyle factors such as smoking, diet and inactivity. Conversely, regular exercise is linked to decreased risk of multiple cancers. However, the underlying molecular mechanisms are not fully elucidated. Specifically, how exercise impacts tumor genomic […]

TITLE: “Automatic analysis of cryo-electron tomography using computer vision and machine learning.”

TITLE: "Multi-tissue single-cell level understanding of Alzheimer’s disease points to the therapeutic potential of nutritional and metabolic modulation." ABSTRACT: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by extracellular amyloid plaque deposition and intracellular neurofibrillary tangles. The direct cause of abnormal protein accumulation and aggregation is largely unknown, and no treatments exist to effectively delay […]

TITLE: "Efficient, Multi Sample Inference of Cancer Phylogeny." ABSTRACT: Cancer is characterized by the ongoing accumulation of somatic mutations that may lead to dysregulated cellular proliferation. The selection of advantageous mutations leads to clonal expansions of progressively more aberrant and fit cancer cells. Reconstructing the evolutionary history of a tumor allows us to understand key […]

TITLE: "ClusterDE: a post-clustering differentially expressed (DE) gene identification method robust to false-positive inflation caused by double-dipping" ABSTRACT: In typical single-cell RNA-seq data analysis, first, a clustering algorithm is applied to cluster cells; then, a statistical method is used to identify the differentially expressed (DE) genes between the cell clusters. However, this common procedure uses the same […]

TITLE: "Germline structural variants shape prostate cancer clinical and molecular evolution." ABSTRACT: Inherited genetic variation profoundly influences cancer risk and outcome. While the impact of germline single nucleotide polymorphisms has been well-studied in several cancer types, the effects of germline structural variants (gSVs) on cancer biology and clinical outcomes is largely unknown. From our cohort […]