Quasar

easy machine learning for biospectroscopy

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

Abstract

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.

Keywords

odprtokodna programska oprema;strojno učenje;vizualno programiranje;analiza podatkov;spektroskopija;open source;machine learning;visual programming;data exploration;data analysis;spectoscopy;

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004.8:543.422.3-74
COBISS:	125220867
ISSN:	2073-4409
Views:	67
Downloads:	18
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	odprtokodna programska oprema;strojno učenje;vizualno programiranje;analiza podatkov;spektroskopija;
Type (COBISS):	Article
Pages:	str. 1-10
Volume:	ǂVol. ǂ10
Issue:	ǂiss. ǂ9
Chronology:	Sep. 2021
DOI:	10.3390/cells10092300
ID:	16736362