Quasar : easy machine learning for biospectroscopy

Toplak, Marko (author), Read, Stuart T. (author), Sandt, Christophe (author), Borondics, Ferenc (author)

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.

• Language: English
• Keywords: open source, machine learning, visual programming, data exploration, data analysis, spectroscopy
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: 10 str.
• Numbering: Vol. 10, iss. 9, art. 2300
• PID: 20.500.12556/RUL-141968
• UDC: 004.8:543.422.3-74
• ISSN on article: 2073-4409
• DOI: 10.3390/cells10092300
• COBISS.SI-ID: 125220867
• Publication date in RUL: 13.10.2022

Record is a part of a journal

• Title: Cells
• Shortened title: Cells
• Publisher: MDPI
• ISSN: 2073-4409
• COBISS.SI-ID: 519958809

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: odprtokodna programska oprema, strojno učenje, vizualno programiranje, analiza podatkov, spektroskopija