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Article

Quasar: Easy Machine Learning for Biospectroscopy

1
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia
2
Canadian Light Source, Inc., 44 Innovation Boulevard, Saskatoon, SK S7N 2V3, Canada
3
SOLEIL Synchrotron, L’Orme des Merisiers, Saint Aubin-BP 48, CEDEX, 91192 Gif sur Yvette, France
*
Author to whom correspondence should be addressed.
Academic Editors: Lisa Vaccari, Hugh J. Byrne and Tomasz P. Wrobel
Cells 2021, 10(9), 2300; https://doi.org/10.3390/cells10092300
Received: 31 July 2021 / Revised: 20 August 2021 / Accepted: 30 August 2021 / Published: 3 September 2021
(This article belongs to the Special Issue Cellular and Subcellular Analysis Using Vibrational Spectroscopy)
Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques. View Full-Text
Keywords: open source; machine learning; visual programming; data exploration; data analysis open source; machine learning; visual programming; data exploration; data analysis
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MDPI and ACS Style

Toplak, M.; Read, S.T.; Sandt, C.; Borondics, F. Quasar: Easy Machine Learning for Biospectroscopy. Cells 2021, 10, 2300. https://doi.org/10.3390/cells10092300

AMA Style

Toplak M, Read ST, Sandt C, Borondics F. Quasar: Easy Machine Learning for Biospectroscopy. Cells. 2021; 10(9):2300. https://doi.org/10.3390/cells10092300

Chicago/Turabian Style

Toplak, Marko, Stuart T. Read, Christophe Sandt, and Ferenc Borondics. 2021. "Quasar: Easy Machine Learning for Biospectroscopy" Cells 10, no. 9: 2300. https://doi.org/10.3390/cells10092300

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