iTraNet is an interactive web application that visualizes and analyzes trans-omics networks involving four major types of networks: gene regulatory networks; protein-protein interactions; metabolic networks; and metabolite exchange networks.
This app focuses on predicting protein-ligand interactions. Bacteria respond to chemical cues using protein transcription regulators. Ligify is a search tool used to mine regulators responsive to user-defined metabolites.
The Drug Hunter web-based application is a powerful tool for predicting pIC50 values for human protein inhibitors (potential new drugs). Based on the QSAR methodology and state-of-the-art machine learning algorithms, as well as a comprehensive database of pro…
This app provides researchers with intuitive tools to explore drug synergy. Through features like dose-response plotting, IC50 curve analysis, and isobologram generation, it allows for a deeper understanding of how drug combinations impact cell survival rates…
Data relating to life circumstances and physiological characteristics are requested in order to obtain an assessment of the risk of stroke. The development of the app is documented in detail on Kaggle. The app is part of a project that shows what needs to be …
This app is a comprehensive tool for exploring and analyzing data from drug screening experiments. It allows users to delve into various aspects of drug effects, from survival rates to behavioral responses and morphological changes in zebrafish larvae. Throug…
The SPT (Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury) web application provides a platform for exploring liver zonation patterns and substrate flow dynamics. This app prov…
This app serves as a valuable tool for visualizing and analyzing serious adverse drug events (ADEs) associated with inflammatory bowel disease (IBD) treatments.
This web app provides a user-friendly interface to predict multiple diseases based on various input features. The machine learning models used in this application are trained on relevant datasets to make accurate predictions. The diseases currently support…
The app created with Python to predict person's heart health condition based on well-trained machine learning model (logistic regression).