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ViVo Platform is a free, open-access web application for the analysis of in vivo tumor growth data in mouse models. Designed to standardize in vivo efficacy analysis, improve research reproducibility, and reduce animal usage, it provides tools to upload tumor volume or bioluminescence imaging (BLI) datasets, configure analysis parameters, detect outliers, and automatically compute tumor growth rates (TGR), group comparisons, and reports. By standardizing data processing and incorporating advanced temporal modeling, ViVo Platform enhances statistical power and contributes to the 3Rs principle by reducing the number of animals needed in preclinical efficacy studies.

pyradiomics is a Python library for extracting radiomics features from medical images. This library was used in a study to predict overall survival in NSCLC patients by integrating clinical, radiomics, and deep learning features.

The Good Statistical Monitoring (gsm) R package is an open-source tool designed to proactively monitor risks in clinical trials, supporting risk-based quality management. It provides a flexible framework for statistical data monitoring and facilitates the creation of standardized reports and interactive visualizations.

CFO is an R package that implements calibration-free odds type Bayesian designs for dose finding in phase I clinical trials, leveraging historical data to identify the maximum tolerated dose (MTD) efficiently and safely. The package includes variants for various scenarios, such as drug-combination trials and late-onset toxicity.

COVIDrugNet is a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19.

Detecting fabrication in large-scale molecular omics data | Fraud is a pervasive problem and can occur as fabrication, falsification, plagiarism or theft. The scientific community is not exempt from this universal problem and several studies have recently been caught manipulating or fabricating data. Current measures to prevent and deter scientific misconduct come in the form of the peer-review process and on-site clinical trial auditors. As recent advances in high-throughput omics technologies have moved biology into the realm of big-data, fraud detection methods must be updated for sophisticated computational fraud | In the financial sector, machine learning and digit-preference are successfully used to detect fraud | See our pre-press article at: https://www.biorxiv.org/content/10.1101/757070v1

An open-source computational pipeline for robust nanobody humanization.