Hypothesis testing

…data import and visualisation skills, hypothesis testing and genomic interval analysis before covering read mapping, count normalisation, differential expression and multiple-testing correction. Practical comparisons include DESeq2, edgeR and limma/voom. The final session interprets functional pathways using overrepresentation and permutation-based gene-set enrichment analysis. Six live online sessions on 2, 3, 5, 6, 9 and 10 November, 15:00–18:00 Europe/Berlin (CET). Fee €530. Basic R programming and

…alignment, phylogenetic inference, hypothesis testing, natural selection, divergence dating and coalescent methods. It focuses on evolutionary analysis of nucleotide and protein sequence datasets rather than genome assembly or annotation. The course particularly suits early- and mid-stage doctoral researchers with some phylogenetics experience, access to their own data and basic Linux familiarity. Participants are selected through an application process.

Navigation is inherently dynamic. It involves learning the environment, as well as positions in and trajectories through it, and then executing a path to reach a target. Spatial navigation skills vary significantly among individuals. But what differentiates a good navigator from a bad one, or an easy-to-navigate path from a hard one? Studies have analysed exploration and navigation using static quantitative measures, e.g., location tallies or distance traveled. However, static metrics are inherently limited for characterisation of dynamic behaviours. To fill this gap, we introduce the Flow Tre

Fast and adaptable learning is often characterized by sudden jumps in performance rather than slow adaptation typical of updating synaptic weights. Cognitive science theories posit that rapid insights are due to compositional symbolic representations that allow animals to quickly recombine past knowledge to adapt to new settings. Traditionally, it has been difficult to model this type of compositional learning with neural systems. Here we leverage recent work on compositional computations in multitasking Recurrent Neural Networks (RNNs) to construct a neural model of compositional learning. Pa

Conventional approaches to hypothesis testing often hinge on linear models like ANOVA, subjective decision-making regarding factors and their interactions, and reliance on in-sample inference without thorough evaluation on hold-out data. These oversimplifications, subjective choices, and limited generalizability may cast doubt on the relevance and validity of scientific theories and conclusions. A transformative shift towards more resilient methodologies entails the incorporation of interpretability techniques, sophisticated machine learning models, and advanced out-of-sample model evaluation