Titlebook: Comparative Genomics; 20th International C Katharina Jahn,Tomá? Vina? Conference proceedings 2023 The Editor(s) (if applicable) and The Aut

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CONSULT-II: Taxonomic Identification Using Locality Sensitive Hashing, of such data. Taxonomic classification requires comparing sample reads against a reference dataset of known organisms. Crucially, the genomes represented in a sample may be phylogenetically distant from their closest match in the reference set. Thus, simply mapping reads to genomes is insufficient;

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,MoTERNN: Classifying the?Mode of?Cancer Evolution Using Recursive Neural Networks, patient. This evolutionary history, which takes the shape of a tree, reveals the mode of evolution of the specific cancer under study and, in turn, helps with clinical diagnosis, prognosis, and therapeutic treatment. In this study we focus on the question of determining the mode of evolution of tum

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Physical Mapping of Two Nested Fixed Inversions in the X Chromosome of the Malaria Mosquito , on gene sequences of the annotated . genome. The two inversions resulted in five syntenic blocks, of which only two syntenic blocks (encompassing at least 179 annotated genes in the . genome) changed their position and orientation in the X chromosome. Analysis of the . genome revealed enrichment of

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,Prior Density Learning in?Variational Bayesian Phylogenetic Parameters Inference,ting branch lengths and evolutionary model parameters. They also show that a flexible prior model could provide better results than a predefined prior model. Finally, the results highlight that using neural networks improves the initialization of the optimization of the prior density parameters.