R.ROSETTA: an interpretable machine learning framework

R.ROSETTA: an interpretable machine learning framework

Abstract Background Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing...

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Bibliographic Details
Main Authors: Mateusz Garbulowski, Klev Diamanti, Karolina Smolińska, Nicholas Baltzer, Patricia Stoll, Susanne Bornelöv, Aleksander Øhrn, Lars Feuk, Jan Komorowski
Format: Article
Language:English
Published: BMC 2021-03-01
Series:BMC Bioinformatics
Subjects:
Transcriptomics
Interpretable machine learning
Big data
Rough sets
Rule-based classification
R package
Online Access:https://doi.org/10.1186/s12859-021-04049-z

Internet

https://doi.org/10.1186/s12859-021-04049-z

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