ArticleFrontiers in genetics2018
MADS-Box Gene Classification in Angiosperms by Clustering and Machine Learning Approaches.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Transcriptomic dissection of floral identity and trichome-derived metabolite pathways in Cannabis sativa L.Planta · 2026Article
- Schizophrenia Biomarkers: Blood Transcriptome Suggests Two Molecular Subtypes.Neuromolecular medicine · 2024Article
- Transcriptional Regulation of Pine Male and Female Cone Initiation and Development: Key Players Identified Through Comparative Transcriptomics.Frontiers in genetics · 2022Article
- Genome-wide study of flowering-related MADS-box genes family in3 Biotech · 2020Article
- A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize.Frontiers in genetics · 2020Article
- Molecular Mechanisms of the Floral Biology ofFrontiers in plant science · 2020Review
- Floral Induction and Flower Development of Orchids.Frontiers in plant science · 2019Review
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Authors and funding
5 authors.
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Abstract
The MADS-box gene family is an important transcription factor family involved in floral organogenesis. The previously proposed ABCDE model suggests that different floral organ identities are controlled by various combinations of classes of MADS-box genes. The five-class ABCDE model cannot cover all the species of angiosperms, especially the orchid. Thus, we developed a two-stage approach for MADS-box gene classification to advance the study of floral organogenesis of angiosperms. First, eight classes of reference datasets (A, AGL6, B12, B34, BPI, C, D, and E) were curated and clustered by phylogenetic analysis and unsupervised learning, and they were confirmed by the literature. Second, feature selection and multiple prediction models were curated according to sequence similarity and the characteristics of the MADS-box gene domain using support vector machines. Compared with the BindN and COILS features, the local BLAST model yielded the best accuracy. For performance evaluation, the accuracy of
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