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Quasar: Easy Machine Learning for Biospectroscopy

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 depe...

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Detalles Bibliográficos
Autores principales: Toplak, Marko, Read, Stuart T., Sandt, Christophe, Borondics, Ferenc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466383/
https://www.ncbi.nlm.nih.gov/pubmed/34571947
http://dx.doi.org/10.3390/cells10092300
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author Toplak, Marko
Read, Stuart T.
Sandt, Christophe
Borondics, Ferenc
author_facet Toplak, Marko
Read, Stuart T.
Sandt, Christophe
Borondics, Ferenc
author_sort Toplak, Marko
collection PubMed
description 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.
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spelling pubmed-84663832021-09-27 Quasar: Easy Machine Learning for Biospectroscopy Toplak, Marko Read, Stuart T. Sandt, Christophe Borondics, Ferenc Cells Article 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. MDPI 2021-09-03 /pmc/articles/PMC8466383/ /pubmed/34571947 http://dx.doi.org/10.3390/cells10092300 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Toplak, Marko
Read, Stuart T.
Sandt, Christophe
Borondics, Ferenc
Quasar: Easy Machine Learning for Biospectroscopy
title Quasar: Easy Machine Learning for Biospectroscopy
title_full Quasar: Easy Machine Learning for Biospectroscopy
title_fullStr Quasar: Easy Machine Learning for Biospectroscopy
title_full_unstemmed Quasar: Easy Machine Learning for Biospectroscopy
title_short Quasar: Easy Machine Learning for Biospectroscopy
title_sort quasar: easy machine learning for biospectroscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466383/
https://www.ncbi.nlm.nih.gov/pubmed/34571947
http://dx.doi.org/10.3390/cells10092300
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