Cargando…
Prediction of protein self-interactions using stacked long short-term memory from protein sequences information
BACKGROUND: Self-interacting Proteins (SIPs) plays a critical role in a series of life function in most living cells. Researches on SIPs are important part of molecular biology. Although numerous SIPs data be provided, traditional experimental methods are labor-intensive, time-consuming and costly a...
Autores principales: | Wang, Yan-Bin, You, Zhu-Hong, Li, Xiao, Jiang, Tong-Hai, Cheng, Li, Chen, Zhan-Heng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302371/ https://www.ncbi.nlm.nih.gov/pubmed/30577794 http://dx.doi.org/10.1186/s12918-018-0647-x |
Ejemplares similares
-
Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform
por: Chen, Zhan-Heng, et al.
Publicado: (2019) -
RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information
por: Yi, Hai-Cheng, et al.
Publicado: (2020) -
Improving Prediction of Self-interacting Proteins Using Stacked Sparse Auto-Encoder with PSSM profiles
por: Wang, Yan-Bin, et al.
Publicado: (2018) -
SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information
por: Ren, Zhong-Hao, et al.
Publicado: (2022) -
A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network
por: Wang, Yan-Bin, et al.
Publicado: (2020)