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A directory of tools, AI models, datasets, and research resources for biotech, bioinformatics, and other scientific fields. Aggregated from curated GitHub awesome-lists, HuggingFace, bio.tools, Bioconductor, and more.
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SuperSurv is an R package that provides a unified framework for building, evaluating, and interpreting ensemble machine learning models for right-censored survival data. It implements techniques like stacking and integrates interpretability tools such as SHAP values and Restricted Mean Survival Time (RMST) contrasts.
Cell Adhesion Supervised Training and Learning Environment (CASTLE) is a series of ImageJ plugins. CASTLE combines effective techniques for phase-contrast image processing with statistical analysis and machine learning algorithms to interpret the results.
The Case for Sensible Extraction of Interactions in the Era of Machine Learning. Extract meaningful interactions from machine learning models to obtain machine-learning performance with statistical model interpretability. Welcome to InteractionTransformer. Welcome to the InteractionTransformer wiki!. What our package does not do: extract transformations in the data to add them to the design matrix. For those capabilities, please see the SAFE Transformer package (https://github.com/ModelOriented/SAFE). Our package does have an experimental feature that will iteratively call InteractionTransformer and SAFETransformer to develop multi-layer interactions/transformation models, but this is still under development.
a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics. single cell graph convolutional network (scGCN). This is a TensorFlow implementation of scGCN for leveraging differnt single cell datasets, which is described in our paper:.