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Nisma Amjad – Research Engineer @ ISIR, Sorbonne University

In the analysis of diverse omics data, a common and important preliminary step involves computing low-dimensional embeddings using techniques such as PCA, UMAP, t-SNE, or variational autoencoders. These embeddings provide a global overview of sample distributions and their relationships, often serving as the basis for formulating biological hypotheses. To facilitate rapid and intuitive exploration of such low-dimensional embeddings, we developed Yomix, a interactive omics-agnostic visualisation and data exploration tool. Yomix enables users to flexibly define subsets of interest using a lasso selection tool, instantly compute their feature signatures, and compare their distributions. Yomix is a fast and efficient tool for interactive exploration of diverse omics datasets.

pca umap t-sne variational autoencoders yomix Python
Dea Maria Leon – Freelance open-source developer @ NumFOCUS

Scikit-learn now makes it easier to explore estimators by displaying their parameter values and allowing them to be copied. In the next release, each parameter will also include a short documentation preview and a link to the full reference page. More enhancements are on the way to make model inspection even richer and more intuitive. This work blends front-end development with Python. Dea's path into open source and the PyData ecosystem started with a desire for a new career direction and a lifelong curiosity for technical challenges.

scikit-learn Python front-end development
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