Browsing by Subject "web application"

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  • Wargelin, Matias (Helsingin yliopisto, 2021)
    Musical pattern discovery refers to the automated discovery of important repeated patterns, such as melodies and themes, from music data. Several algorithms have been developed to solve this problem, but evaluating the algorithms has been difficult without proper visualisations of the output of the algorithms. To address this issue a web application named Mupadie was built. Mupadie accepts MIDI music files as input and visualises the outputs of musical pattern discovery algorithms, with implementations of SIATEC and TTWIA built in the application. Other algorithms can be visualised if the algorithm output is uploaded to Mupadie as a JSON file that follows a specified data structure. Using Mupadie, an evaluation of SIATEC and TTWIA was conducted. Mupadie was found to be a useful tool in the qualitative evaluation of these musical pattern discovery algorithms; it helped reveal systematically recurring issues with the discovered patterns, some previously known and some previously undocumented. The findings were then used to suggest improvements to the algorithms.
  • Abudahab, Khalil; Prada, Joaquin M.; Yang, Zhirong; Bentley, Stephen D.; Croucher, Nicholas J.; Corander, Jukka; Aanensen, David M. (2019)
    The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here, we introduce PANINI (Pangenome Neighbour Identification for Bacterial Populations), a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding based on the t-SNE (t-distributed stochastic neighbour embedding) algorithm. PANINI is browser-based and integrates with the Microreact platform for rapid online visualization and exploration of both core and accessory genome evolutionary signals, together with relevant epidemiological, geographical, temporal and other metadata. Several case studies with single- and multi-clone pneumococcal populations are presented to demonstrate the ability to identify biologically important signals from gene content data. PANINI is available at http://panini.pathogen.watch and code at http://gitlab.com/cgps/panini.