ArticlePloS one2014
Using multi-instance hierarchical clustering learning system to predict yeast gene function.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- A tensor-based bi-random walks model for protein function prediction.BMC bioinformatics · 2022Article
- Transcriptional response to Wnt activation regulates the regenerative capacity of the mammalian cochlea.Development (Cambridge, England) · 2018Article
- Gene function prediction based on combining gene ontology hierarchy with multi-instance multi-label learning.RSC advances · 2018Article
- Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM.BMC bioinformatics · 2014Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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Abstract
Time-course gene expression datasets, which record continuous biological processes of genes, have recently been used to predict gene function. However, only few positive genes can be obtained from annotation databases, such as gene ontology (GO). To obtain more useful information and effectively predict gene function, gene annotations are clustered together to form a learnable and effective learning system. In this paper, we propose a novel multi-instance hierarchical clustering (MIHC) method to establish a learning system by clustering GO and compare this method with other learning system establishment methods. Multi-label support vector machine classifier and multi-label K-nearest neighbor classifier are used to verify these methods in four yeast time-course gene expression datasets. The MIHC method shows good performance, which serves as a guide to annotators or refines the annotation in detail.
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