To make the whole analysis procedure more accessible for clinical samples, the algorithm can quantify ploidy information using hematoxylin-eosin (H&E) histopathology slides. A deep-learning model was trained to segment and classify different types of nuclei from H&E histopathology images. Based on the identified hepatocyte nuclei, both cellular and nuclear ploidy are calculated.

Human evaluation is objective and labor-intensive. In addition, pathological images may contain important prognosis and treatment response information that cannot be reliably understood by humans. Therefore, it is a very important to create machine learning algorithms to automatically recognize pathological imaging data and correlate the automatically retrieved features with patient phenotype.

SAFARI (Shape Analysis For AI-Segmented Images) provides functionality for image processing and shape analysis. In the context of reconstructed medical images generated by deep learning-based methods and produced from different modalities such as X-ray, Computational Tomography (CT), Magnetic Resonance Imaging (MRI), and pathology imaging, SAFARI offers tools to segment regions of interest and extract quantitative shape descriptors for applications in signal processing, statistical analysis and modeling, and machine learning.

Microbiome Community Detector (MiCoDe) is a web tool that can be used to investigate the community structure of co-occurrence networks estimated from microbiome data. MiCoDe fits either a Bayesian nonparametric weighted stochastic block model (WSBM) or a Bayesian nonparametric weighted stochastic infinite block model (WSIBM) to a fully connected network represented by a graph with weighted edges.

We provide and consistently support online tools, prediction models and software packages. You can download them from the website link below.