Brain Predictability toolbox: a Python library for neuroimaging based machine learning
Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (in particular brain, psychiatric, behavioral, and physiological variables) and neuroimaging specific derived data (e.g., brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging based ML questions, in particular, those queried from large human datasets. Availability and Implementation BPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app. Contact Please contact Sage Hahn at sahahn@uvm.edu
Code (2)
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms
Hyperbox-based machine learning algorithms are an important and popular branch of machine learning in the construction of classifiers using fuzzy sets and logic theory and neural network architectures. This type of learn…
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting…
BIG-bench Machine LearningTime SeriesTime Series AnalysisADAPT : Awesome Domain Adaptation Python Toolbox
In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods. The library is designed with a user friendly approach to …
Domain AdaptationTransfer LearningMachine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
We consider a problem of diagnostic pattern recognition/classification from neuroimaging data. We propose a common data analysis pipeline for neuroimaging-based diagnostic classification problems using various ML algorit…
BIG-bench Machine LearningClassificationDiagnosticGeneral ClassificationSPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages. Existing implementations often employ ad hoc handling…