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Multi‐label learning: a review of the state of the art and ongoing research
Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …
to improve performance in problems where a pattern may have more than one associated …
Supervised learning with decision tree-based methods in computational and systems biology
At the intersection between artificial intelligence and statistics, supervised learning allows
algorithms to automatically build predictive models from just observations of a system …
algorithms to automatically build predictive models from just observations of a system …
A survey of hierarchical classification across different application domains
In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …
is scattered across very different application domains and for that reason research in one …
Hierarchical text classification with reinforced label assignment
While existing hierarchical text classification (HTC) methods attempt to capture label
hierarchies for model training, they either make local decisions regarding each label or …
hierarchies for model training, they either make local decisions regarding each label or …
Hierarchical multi-label prediction of gene function
Motivation: Assigning functions for unknown genes based on diverse large-scale data is a
key task in functional genomics. Previous work on gene function prediction has addressed …
key task in functional genomics. Previous work on gene function prediction has addressed …
[HTML][HTML] Hierarchical multi-label classification using local neural networks
Hierarchical multi-label classification is a complex classification task where the classes
involved in the problem are hierarchically structured and each example may simultaneously …
involved in the problem are hierarchically structured and each example may simultaneously …
Predicting gene function using hierarchical multi-label decision tree ensembles
Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in
biology and the sequencing of their genomes was completed many years ago. It is still a …
biology and the sequencing of their genomes was completed many years ago. It is still a …
Finding function: evaluation methods for functional genomic data
Background Accurate evaluation of the quality of genomic or proteomic data and
computational methods is vital to our ability to use them for formulating novel biological …
computational methods is vital to our ability to use them for formulating novel biological …
Automated prediction of protein function and detection of functional sites from structure
Current structural genomics projects are yielding structures for proteins whose functions are
unknown. Accordingly, there is a pressing requirement for computational methods for …
unknown. Accordingly, there is a pressing requirement for computational methods for …
The use of classification trees for bioinformatics
Classification trees are nonparametric statistical learning methods that incorporate feature
selection and interactions, possess intuitive interpretability, are efficient, and have high …
selection and interactions, possess intuitive interpretability, are efficient, and have high …