Metadata-Version: 2.1
Name: eslearn
Version: 1.0.5b0
Summary: This project is designed for machine learning in resting-state fMRI field
Home-page: https://github.com/easylearn-fmri/
Author: Chao Li; Mengshi Dong
Author-email: lichao19870617@gmail.com
Maintainer: Chao Li; Mengshi Dong
Maintainer-email: lichao19870617@gmail.com
License: MIT License
Description: Easylearn is designed for machine learning mainly in resting-state fMRI, radiomics and other fields (such as EEG). Easylearn is built on top of scikit-learn, pytorch and other packages. Easylearn can assist doctors and researchers who have limited coding experience to easily realize machine learning, e.g., (MR/CT/PET/EEG)imaging-marker- or other biomarker-based disease diagnosis and prediction, treatment response prediction, disease subtyping, dimensional decoding for transdiagnostic psychiatric diseases or other diseases, disease mechanism exploration and etc.  
        
        We focus on machine learning rather than data preprocessing. Many software, such as SPM, GRETNA, DPABI, REST, RESTPlus, CCS, FSL, Freesufer, nipy, nipype, nibabel, fmriprep and etc, can be used for data preprocessing.  
        
        # Citing information:
        If you think this software (or some function) is useful, citing the easylearn software in your paper or code would be greatly appreciated!
        Citing link: https://github.com/easylearn-fmri/easylearn  
        
        # Core Dependencies 
        The follows will be automatically install if you use "pip install -U easylearn" command    
        
        - numpy
        - pandas
        - python-dateutil
        - pytz
        - scikit-learn
        - scipy
        - six
        - nibabel
        - imbalanced-learn
        - skrebate
        - matplotlib
        
        # Install  
        ```
        pip install eslearn
        ```
        
        # Usage
        ```
        from eslearn.GUI.easylearn_main_run import main
        main()
        ```
        
        # Development    
        At present, the project is in the development stage. We hope you can join us!   
        Any contributions you make will be appreciated and announced.   
        Please refer to [developer link](https://github.com/easylearn-fmri/easylearn/tree/master/eslearn/developer) for details.
        > Email: lichao19870617@gmail.com  
        > Wechat: 13591648206  
        
        # Initiators
        ##### Ke Xu
            kexu@vip.sina.com  
            Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.  
            Department of Radiology, The First Affiliated Hospital of China Medical University.
        
        ##### Chao Li
            lichao19870617@gmail.com
            Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.  
            
        ##### Mengshi Dong
            dongmengshi1990@163.com  
            Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China.   
        
        # Supervisors/Consultants 
        ##### Yanqing Tang  
            yanqingtang@163.com  
            1 Brain Function Research Section, The First Affiliated Hospital of China Medical
            University, Shenyang, Liaoning, PR China.  
            2 Department of Psychiatry, The First Affiliated Hospital of China Medical University,
            Shenyang, Liaoning, PR China.        
            
        ##### Yong He  
            yong.he@bnu.edu.cn  
            1 National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China  
            2 Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China  
            3 IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China 
        
        # Maintainers
        ##### Vacancy 1   
        Contributors will first add to the [contributors_list.md](./eslearn/developer/contributors_list.md). Once your contribution is important or more than or equal to 1/4 of the total code, we will add you as a maintainer.  
        
        ##### Vacancy 2  
        Contributors will first add to the [contributors_list.md](./eslearn/developer/contributors_list.md). Once your contribution is important or more than or equal to 1/4 of the total code, we will add you as a maintainer. 
        
        # Contributors  
        The current contributors are in [contributors_list.md](./eslearn/developer/contributors_list.md). Once your contribution is important or more than or equal to 1/4 of the total code, we will add you as a maintainer. 
        
        # Curent team members
        The current team members are in [current_team_members.md](./eslearn/developer/current_team_members.md). If you contributed your code, please add yourself to the contributor list.
Platform: all
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Natural Language :: English
Classifier: Natural Language :: Chinese (Simplified)
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
