Search: onr:"swepub:oai:lup.lub.lu.se:046f029e-97c1-423e-aee5-77c9ec34cbfd" >
multiclassPairs: An...
multiclassPairs: An R package to train multiclass pair-based classifier
-
- Marzouka, Nour-Al-Dain (author)
- Lund University,Lunds universitet,Genomiska analyser av urinblåscancer,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Urinblåsecancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Urothelial Cancer Genomics,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Urothelial cancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
-
- Eriksson, Pontus (author)
- Lund University,Lunds universitet,Genomiska analyser av urinblåscancer,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Urinblåsecancer,Sektion I,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Urothelial Cancer Genomics,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Urothelial cancer,Section I,Department of Clinical Sciences, Lund,Faculty of Medicine
-
(creator_code:org_t)
- 2021-02-05
- 2021
- English.
-
In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059. ; 37:18, s. 3043-3044
- Related links:
-
http://dx.doi.org/10... (free)
-
show more...
-
https://academic.oup...
-
https://lup.lub.lu.s...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- Motivationk–Top Scoring Pairs (kTSP) algorithms utilize in-sample gene expression feature pair rules for class prediction, and have demonstrated excellent performance and robustness. The available packages and tools primarily focus on binary prediction (i.e. two classes). However, many real-world classification problems e.g., tumor subtype prediction, are multiclass tasks.ResultsHere, we present multiclassPairs, an R package to train pair-based single sample classifiers for multiclass problems. multiclassPairs offers two main methods to build multiclass prediction models, either using a one-vs-rest kTSP scheme or through a novel pair-based Random Forest approach. The package also provides options for dealing with class imbalances, multiplatform training, missing features in test data, and visualization of training and test results.Availability‘multiclassPairs’ package is available on CRAN servers and GitHub: https://github.com/NourMarzouka/multiclassPairsSupplementary informationSupplementary data are available at Bioinformatics online.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Publication and Content Type
- art (subject category)
- ref (subject category)
Find in a library
To the university's database