Cargando…
Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method
Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However there is still room for improvement toward the theoret...
Autores principales: | Ma, Yuming, Liu, Yihui, Cheng, Jinyong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026213/ https://www.ncbi.nlm.nih.gov/pubmed/29959372 http://dx.doi.org/10.1038/s41598-018-28084-8 |
Ejemplares similares
-
Subspace-by-subspace preconditioners for structured linear systems
por: Daydé, M J, et al.
Publicado: (1998) -
DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system
por: Yuan, Lu, et al.
Publicado: (2022) -
Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory
por: Yuan, Lu, et al.
Publicado: (2023) -
Extended Averaged Learning Subspace Method for Hyperspectral Data Classification
por: Bagan, Hasi, et al.
Publicado: (2009) -
Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles
por: Yang, Liying, et al.
Publicado: (2016)