Mondo Disease Ontology

Repository for the Human Disease Ontology.

A cancer staging client library for Java applications.

The symptom ontology was designed around the guiding concept of a symptom being: “A perceived change in function, sensation or appearance reported by a patient indicative of a disease”. Understanding the close relationship of Signs and Symptoms, where Signs are the objective observation of an illness, the Symptom Ontology will work to broaden it…

Disease Diagnosis using Pomegranate

API for Current cases and more stuff about COVID-19 and Influenza

MyoQuant🔬: a tool to automatically quantify pathological features in muscle fiber histology images. Demo version deployed at: https://lbgi.fr/MyoQuant

IMPatienT🗂️: an integrated web application to digitize, process and explore multimodal patient data. Demo version deployed at: https://impatient.lbgi.fr/

Ontology for drivers and triggers of human diseases, built to classify ExO ontology exposure stressors. An application ontology. Built in collaboration with EnvO, ExO, ECTO and ChEBI.

Healthcare system to predict Diseases based on patient symptoms

A desktop application to fetch Wikipedia,Google,Disease results and save them as text file,in database.Have a Section to search details about doctors in location

This project implements a Disease Prediction System using various machine learning algorithms to predict potential diseases based on user-provided symptoms. The system utilizes a Django web framework to provide a user-friendly interface for inputting symptoms and viewing the predicted disease.

Crop-Disease-Identification leverages Fastai and deep learning techniques to efficiently detect and classify crop diseases from images, facilitating rapid diagnosis and precision agricultural management.

Paper - Threat of white-nose syndrome to naive southern hemisphere bats

Using advanced ML algorithms, it predict multiple diseases simultaneously, considering factor like medical history and genetics, This approach overcomes traditional diagnostic limitations, offering insights for early detection and personalized treatments, ultimately enhancing healthcare outcomes.

Analysis of various genetic backgrounds to help elucidate aspects of Rett syndrome

Investigating how carbon emissions, particulate matter, and climate variables/indices impact mortality from chronic respiratory disease. Working with pollutant, climate, mortality, population, and geographic datasets. Modeling with Random Forest regression.

This python script detects known traits in raw human genetic data.

Bases on Leaf images we are trying to predict plant disease using convolutional neural network. PyTorch implementation