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1.
Bioinformatics ; 39(1)2023 01 01.
Article in English | MEDLINE | ID: mdl-36445000

ABSTRACT

MOTIVATION: We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data. RESULTS: dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. AVAILABILITY AND IMPLEMENTATION: dnadna is a Python (≥3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.


Subject(s)
Deep Learning , Reproducibility of Results , Neural Networks, Computer , Software , Genetics, Population
2.
IEEE Comput Graph Appl ; 41(2): 76-88, 2021.
Article in English | MEDLINE | ID: mdl-33095705

ABSTRACT

We describe Cartolabe, a web-based multiscale system for visualizing and exploring large textual corpora based on topics, introducing a novel mechanism for the progressive visualization of filtering queries. Initially designed to represent and navigate through scientific publications in different disciplines, Cartolabe has evolved to become a generic framework and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries). Cartolabe is made of two modules: The first relies on natural language processing methods, converting a corpus and its entities (documents, authors, and concepts) into high-dimensional vectors, computing their projection on the two-dimensional plane, and extracting meaningful labels for regions of the plane. The second module is a web-based visualization, displaying tiles computed from the multidimensional projection of the corpus using the Umap projection method. This visualization module aims at enabling users with no expertise in visualization and data analysis to get an overview of their corpus, and to interact with it: exploring, querying, filtering, panning, and zooming on regions of semantic interest. Three use cases are discussed to illustrate Cartolabe's versatility and ability to bring large-scale textual corpus visualization and exploration to a wide audience.

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