Multiview: a software package for multiview pattern recognition methods

• Published in: Bioinformatics (Oxford, England) Vol. 35; no. 16; pp. 2877 - 2879
• Main Authors: Kanaan-Izquierdo, Samir, Ziyatdinov, Andrey, Burgueño, Maria Araceli, Perera-Lluna, Alexandre
• Format: Journal Article Publication
• Language: English
• Published: England 15.08.2019
• Subjects: Aplicacions de la informàtica; Bioinformatics; Bioinformàtica; clustering; dimensionality reduction; Informàtica; Multiview data processing; Àrees temàtiques de la UPC
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/bty1039

Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview data are gathered from several experiments, measurements or feature sets available for the same subjects. Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering and dimensionality reduction; however, none of these methods are readily available to the extent of our knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering method. Often they produce better results than their single-view counterparts, tested here on four multiview datasets. R package at the B2SLab site: http://b2slab.upc.edu/software-and-tutorials/ and Python package: https://pypi.python.org/pypi/multiview. Supplementary data are available at Bioinformatics online.