Brain Predictability toolbox: a Python library for neuroimaging based machine learning

• URL: http://arxiv.org/abs/2011.01715v1
• Date: Tue, 3 Nov 2020 14:06:43 GMT
• Title: Brain Predictability toolbox: a Python library for neuroimaging based machine learning
• Authors: Sage Hahn, Dekang Yuan, Wesley Thompson, Max M Owens, Nicholas Allgaier and Hugh Garavan
• Abstract summary: Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools. This package is suitable for investigating a wide range of different neuroimaging based ML questions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification [0.4038539043067986]
  cuvis.ai is an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training. The package is written in Python and provides wrappers around common machine learning libraries.
  arXiv  Detail & Related papers  (2024-11-18T06:33:40Z)
• Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping [0.0]
  The Deep Fast Machine Learning Utils (DFMLU) library provides tools designed to automate and enhance aspects of machine learning processes. DFMLU offers functionalities that support model development and data handling. This manuscript presents an overview of DFMLU's functionalities, providing Python examples for each tool.
  arXiv  Detail & Related papers  (2024-09-14T21:39:17Z)
• Comgra: A Tool for Analyzing and Debugging Neural Networks [35.89730807984949]
  We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations of a model and organizes it in a GUI. It can show both summary statistics and individual data points, compare early and late stages of training, focus on individual samples of interest, and visualize the flow of the gradient through the network.
  arXiv  Detail & Related papers  (2024-07-31T14:57:23Z)
• TopoX: A Suite of Python Packages for Machine Learning on Topological Domains [89.9320422266332]
  TopoX is a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains. TopoX consists of three packages: TopoNetX, TopoEmbedX and TopoModelx.
  arXiv  Detail & Related papers  (2024-02-04T10:41:40Z)
• Causal-learn: Causal Discovery in Python [53.17423883919072]
  Causal discovery aims at revealing causal relations from observational data. $textitcausal-learn$ is an open-source Python library for causal discovery.
  arXiv  Detail & Related papers  (2023-07-31T05:00:35Z)
• hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms [9.061408029414455]
  hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms. hyperbox-brain exposes a unified API which closely follows and is compatible with the renowned scikit-learn and numpy toolboxes.
  arXiv  Detail & Related papers  (2022-10-06T06:40:07Z)
• DADApy: Distance-based Analysis of DAta-manifolds in Python [51.37841707191944]
  DADApy is a python software package for analysing and characterising high-dimensional data. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for comparing different distance metrics.
  arXiv  Detail & Related papers  (2022-05-04T08:41:59Z)
• PyHHMM: A Python Library for Heterogeneous Hidden Markov Models [63.01207205641885]
  PyHHMM is an object-oriented Python implementation of Heterogeneous-Hidden Markov Models (HHMMs) PyHHMM emphasizes features not supported in similar available frameworks: a heterogeneous observation model, missing data inference, different model order selection criterias, and semi-supervised training. PyHHMM relies on the numpy, scipy, scikit-learn, and seaborn Python packages, and is distributed under the Apache-2.0 License.
  arXiv  Detail & Related papers  (2022-01-12T07:32:36Z)
• pymdp: A Python library for active inference in discrete state spaces [52.85819390191516]
  pymdp is an open-source package for simulating active inference in Python. We provide the first open-source package for simulating active inference with POMDPs.
  arXiv  Detail & Related papers  (2022-01-11T12:18:44Z)
• IMBENS: Ensemble Class-imbalanced Learning in Python [26.007498723608155]
  imbens is an open-source Python toolbox for implementing and deploying ensemble learning algorithms on class-imbalanced data. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2021-11-24T20:14:20Z)
• PyHealth: A Python Library for Health Predictive Models [53.848478115284195]
  PyHealth is an open-source Python toolbox for developing various predictive models on healthcare data. The data preprocessing module enables the transformation of complex healthcare datasets into machine learning friendly formats. The predictive modeling module provides more than 30 machine learning models, including established ensemble trees and deep neural network-based approaches.
  arXiv  Detail & Related papers  (2021-01-11T22:02:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.