scholarly journals Design of a graphical user interface for few-shot machine learning classification of electron microscopy data

2022 ◽  
Vol 203 ◽  
pp. 111121
Author(s):  
Christina Doty ◽  
Shaun Gallagher ◽  
Wenqi Cui ◽  
Wenya Chen ◽  
Shweta Bhushan ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
User Interface ◽  
Graphical User Interface ◽  
Machine Learning Classification ◽  
Electron Microscopy Data ◽  
Microscopy Data

PM-02 Analysis of Lithium Ion Battery Materials Using Statistical Machine Learning and Image Processing of Electron Microscopy Data

Microscopy ◽  
2019 ◽  
Vol 68 (Supplement_1) ◽  
pp. i35-i35
Author(s):  
Hiromochi Tanaka ◽  
Tetsushi Watari ◽  
Takahiro Tsubouchi ◽  
Hisao Yamashige ◽  
Takashi Kato ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Image Processing ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Statistical Machine Learning ◽  
Battery Materials ◽  
Electron Microscopy Data ◽  
Microscopy Data

scholarly journals HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0246039
Author(s):  
Shilan S. Hameed ◽  
Rohayanti Hassan ◽  
Wan Haslina Hassan ◽  
Fahmi F. Muhammadsharif ◽  
Liza Abdul Latiff
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Graphical User Interface ◽  
Efficient Algorithm ◽  
Gene Selection ◽  
High Dimensional ◽  
Competitive Performance ◽  
User Friendly ◽  
High Dimensional Datasets

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.

scholarly journals ClassificaIO: machine learning for classification graphical user interface

2017 ◽  
Author(s):  
Raeuf Roushangar ◽  
George I. Mias
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Graphical User Interface ◽  
Machine Learning Algorithms ◽  
Machine Learning Classification ◽  
Link Type ◽  
Research Areas ◽  
Testing Data ◽  
Microsoft Windows ◽  
Python Package

AbstractMachine learning methods are being used routinely by scientists in many research areas, typically requiring significant statistical and programing knowledge. Here we present ClassificaIO, an open-source Python graphical user interface for machine learning classification for the scikit-learn Python library. ClassificaIO provides an interactive way to train, validate, and test data on a range of classification algorithms. The software enables fast comparisons within and across classifiers, and facilitates uploading and exporting of trained models, and both validation and testing data results. ClassificaIO aims to provide not only a research utility, but also an educational tool that can enable biomedical and other researchers with minimal machine learning background to apply machine learning algorithms to their research in an interactive point-and-click way. The ClassificaIO package is available for download and installation through the Python Package Index (PyPI) (http://pypi.python.org/pypi/ClassificaIO) and it can be deployed using the “import” function in Python once the package is installed. The application is distributed under an MIT license and the source code is publicly available for download (for Mac OS X, Linux and Microsoft Windows) through PyPI and GitHub (http://github.com/gmiaslab/ClassificaIO, andhttps://doi.org/10.5281/zenodo.1320465).

scholarly journals Modeling Experimental Image Formation for Likelihood-Based Classification of Electron Microscopy Data

Structure ◽  
2007 ◽  
Vol 15 (10) ◽  
pp. 1167-1177 ◽  
Author(s):  
Sjors H.W. Scheres ◽  
Rafael Núñez-Ramírez ◽  
Yacob Gómez-Llorente ◽  
Carmen San Martín ◽  
Paul P.B. Eggermont ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Formation ◽  
Electron Microscopy Data ◽  
Experimental Image ◽  
Microscopy Data

Classification of single-projection reconstructions for cryo-electron microscopy data of icosahedral viruses

2005 ◽  
Vol 151 (1) ◽  
pp. 79-91 ◽  
Author(s):  
Sjors H.W. Scheres ◽  
Roberto Marabini ◽  
Salvatore Lanzavecchia ◽  
Francesca Cantele ◽  
Twan Rutten ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Cryo Electron Microscopy ◽  
Electron Microscopy Data ◽  
Icosahedral Viruses ◽  
Microscopy Data
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