BrainSort: a Machine Learning Toolkit for Brain Connectome Data Analysis and Visualization

Miaomiao Liu, Tiantian Liu, Yonghao Wang, Yuan Feng, Yunyan Xie, Tianyi Yan, Jinglong Wu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In recent years, applying machine learning methods to neurological and psychiatric disorder diagnoses has grasped the interest of many researchers; however, currently available machine learning toolboxes usually require somewhat intermediate programming knowledge. In order to use machine learning methods more quickly and conveniently, we developed an intuitive toolbox named BrainSort. BrainSort used Python as the main programming languages and employed a hospitable Graphical User Interface (GUI). The toolbox is user-friendly for researchers and clinical doctors with little to no prior programming skills. It enables the client to choose from multiple machine learning methods, such as support vector machine (SVM), k-nearest neighbors (k-NN), and convolutional neural network (CNN) for data processing and training. Using BrainSort, doctors and researchers can calculate and visualize the correlation between brain connectome topology parameters and the symptom in question without prolonged programming training, empowering them to use the powerful tool of machine learning in their studies and practices.

Original languageEnglish
JournalJournal of Signal Processing Systems
Publication statusAccepted/In press - 2020


  • Biomedical image processing
  • Classification algorithms
  • Data visualization
  • Graphical user interfaces
  • Support vector machines

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Hardware and Architecture


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