R.ROSETTA: an interpretable machine learning framework

R.ROSETTA: an interpretable machine learning framework

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 what pre...

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Bibliographic Details
Main Authors: Baltzer, N. (Author), Bornelöv, S. (Author), Diamanti, K. (Author), Feuk, L. (Author), Garbulowski, M. (Author), Komorowski, J. (Author), Øhrn, A. (Author), Smolińska, K. (Author), Stoll, P. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Big data
Bioinformatics analysis
biology
case control study
Case-Control Studies
Computational Biology
data mining
Data mining
Data Mining
Data mining and knowledge discovery
Decision tables
Diseases
Genes
Interpretable machine learning
Knowledge based systems
machine learning
Machine learning
Machine Learning
Machine learning models
Predictive mechanisms
R package
Rough set theory
Rough sets
Rule-based classification
Scientific community
Statistical properties
Transcriptome datum
Transcriptomics
Visualization tools
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