Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM
Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on...
Main Authors: | Zhen-Guo Gao, Lei Wang, Shi-Xiong Xia, Zhu-Hong You, Xin Yan, Yong Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2016-01-01
|
Series: | BioMed Research International |
Online Access: | http://dx.doi.org/10.1155/2016/4563524 |
Similar Items
-
EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation
by: Jiyun Zhou, et al.
Published: (2017-08-01) -
An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model
by: Yang Li, et al.
Published: (2019-07-01) -
Correction of Bias in Estimating Autocovariance Function
by: Wu, Len-Hong
Published: (1983) -
The Structure of Autocovariance Matrix of Discrete Time Subfractional Brownian Motion
by: Guo Jiang
Published: (2018-01-01) -
Effect of neural connectivity on autocovariance and cross covariance estimates
by: Stecker Mark M
Published: (2007-01-01)