Popular GitHub Repositories for Disorders

Awesome graph neural networks for brain network learning. Collections of related research papers with implementations, commonly used datasets and tools. We also invite researchers interested in brain graph learning with GNNs to join the project.

This repo is the official implementation of "Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks" published in IEEE Transactions on Medical Imaging.

Python Machine Learning Toolbox for Brain Network Classification. Source codes are included of the top 20 teams in the Kaggle competition.

Machine Learning (ML) research within medicine and healthcare represents one of the most challenging domains for both engineers and medical specialists. One of the most desired tasks to be accomplished using ML applications is represented by disease detection. A good example of such a task is the detection of genetic abnormalities like Down syndrome, Klinefelter syndrome or Hemophilia. Usually, clinicians are doing chromosome analysis using the karyotype to detect such disorders. The main contribution of the current article consists of introducing a new approach called KaryML Framework, which is extending our previous research: KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps . Our major goal is to provide a new method for an automated karyotyping system using unsupervised techniques. Addit

In this project, we wish to identify psychiatric disorders through patient's speech

scCIPHER: Contextual deep learning on single-cell-enriched knowledge graphs in neurological disorders

Named Entity Recognition for Disorder, Species, Chemical or Drug, Gene and Protein, Enzyme, Anatomy, Biological Process, Molecular Function, Cellular Component, Pathway and microRNAs.

This repository contains the implementation of "Multi-label detection of ophthalmic disorders using InceptionResNetV2 on multiple datasets" paper.

Prihub-Cognitive-Support (Acehack3.0 Innovation Challenge) is committed to enhancing the lives of affected by cognitive disabilities by providing In-depth information on cognitive conditions such as ADHD, Autism spectrum Disorders, Learning Disabilities, Neurocognitive Memory & Developmental Disorders, Dementia, and Traumatic brain injuries.

Welcome to the Federated Learning for Rare Genetic Disorder Classification project! This repository demonstrates the application of Federated Learning (FL) techniques to classify rare genetic disorders using Electronic Health Record (EHR) data while addressing two critical challenges: privacy and limited data availability.

Deep Learning (CNN) for prediction of severity in subjects suffering from sleep breathing disorders.

Cognitive robot to diagnose disorders of motor skills and coordination in children

Knowledge-Based Expert System for the Classification of Mental Disorders using Python