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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.

Train neural networks based on geographic species occurrences, environmental data and existing IUCN Red List assessments to predict the conservation status of "Not Evaluated" or "Data Deficient" species, for any taxon or geographic region of interest.

Interpreting tree ensemble machine learning models with endoR.

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.

CoCoMoRP is a tool for predicting community mortality risk due to CoVID-19 using machine learning and development of a prediction tool.

Biologically Annotated Neural Networks (BANNs), a novel probabilistic framework that makes machine learning fully amenable for GWA applications. BANNs are feedforward models with partially connected architectures that are based on biological annotations.

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.

Disease subnetwork detection with explainable Graph Neural Networks.

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:.