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behavioralproject

Classify TD vs ASD according to SRS behavioral report severity score. ABIDE II data set is utilized for training and testing. Freesurfer v6 is utilized for sMRI volumes preprocessing and features extraction.

https://github.com/silk1100/behavioralproject

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.5%) to scientific vocabulary

Keywords

autism-spectrum-disorder data-analysis feature-engineering feature-selection machine-learning neuroimaging visualization
Last synced: 6 months ago · JSON representation ·

Repository

Classify TD vs ASD according to SRS behavioral report severity score. ABIDE II data set is utilized for training and testing. Freesurfer v6 is utilized for sMRI volumes preprocessing and features extraction.

Basic Info
  • Host: GitHub
  • Owner: silk1100
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 45.8 MB
Statistics
  • Stars: 4
  • Watchers: 3
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
autism-spectrum-disorder data-analysis feature-engineering feature-selection machine-learning neuroimaging visualization
Created almost 5 years ago · Last pushed almost 4 years ago
Metadata Files
Readme Citation

ReadME.txt 

*** Structure

** Core/Motors/Algorithms/Models
	* Experiment (main_experiments.py): The main class that combines all other modules, classes, and functions to build an experiment.
	* DataDivisor (DataDivisor.py): The data handler class. It reads the data directory input as specified in constants.py, and outputs
	* FeatureSelector (FeatureSelection.py): Based upon recursive feature elimination using cross-validation algorithm. This class initialize and train various classifiers that supports feature_importance_ or feature_coef_
	* CustomClassifier (CLassifiers.py): The machine learning classifiers bag that initialize and train different machine learning classifiers
** Configuration/Variablenames/Directories
	* constansts.py: It contains all the keywords used to refer to each attribute in the behavioral report.
			 Contains the keywords used to refer to available classifiers for both ML and feature selection.
			 Contains all directories utilized in saving

** Utility functions
	* utils.py: Contains the functions used to give a time stamp for each result folder created. COntain the functions that is used for saving different FS and ML models.

** Starting point:
	* main.py: It contains only 1 line of code and a dictionary contains all the required experiment attributes.
		   Experimental attributes can be found in experiment_designer.py.

Owner

  • Login: silk1100
  • Kind: user
  • Location: Louisville, KY
  • Company: University of Louisville

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Ali 
    given-names: Mohamed T. 
    orcid: https://orcid.org/0000-0003-4925-7248
title: "Personalized Classification of Behavioral Severity of ASD"
version: 0.0.1
doi:
date-released:

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Dependencies

src/package.json npm