MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.

Background: Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from.

Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.

Background: Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs.

Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b.

Background: A key histopathological hallmark of Alzheimer's disease (AD) is the presence of neurofibrillary tangles of aggregated microtubule-associated protein tau in neurons. Anle138b is a small molecule which has previously shown efficacy in mice in reducing tau aggregates and rescuing AD disease phenotypes.

Methods: In this work, we employed bioinformatics analysis-including pathway enrichment and causal reasoning-of an in vitro tauopathy model.

Adherence to thrombophilia testing guidelines and its influence on anticoagulation therapy: A single-center cross-sectional study.

Introduction: The collected evidence on thrombophilia guidelines is scarce and data about their impact on clinical decisions are unknown. We aimed to investigate the adherence to thrombophilia testing guidelines, its therapeutic impact in patients with guideline-adherent and non-adherent testing and identify the patients' clinical characteristics mostly associated with treatment decisions.

Materials And Methods: We conducted a single-center cross-sectional study of patients referred for thrombophilia testing at the outpatient clinic of a tertiary hospital between 01/2010-10/2020.