TY - JOUR
T1 - Highly Efficient Framework for Predicting Interactions Between Proteins
AU - You, Zhu Hong
AU - Zhou, Meng Chu
AU - Luo, Xin
AU - Li, Shuai
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3
Y1 - 2017/3
N2 - Protein-protein interactions (PPIs) play a central role in many biological processes. Although a large amount of human PPI data has been generated by high-throughput experimental techniques, they are very limited compared to the estimated 130 000 protein interactions in humans. Hence, automatic methods for human PPI-detection are highly desired. This work proposes a novel framework, i.e., Low-rank approximation-kernel Extreme Learning Machine (LELM), for detecting human PPI from a protein's primary sequences automatically. It has three main steps: 1) mapping each protein sequence into a matrix built on all kinds of adjacent amino acids; 2) applying the low-rank approximation model to the obtained matrix to solve its lowest rank representation, which reflects its true subspace structures; and 3) utilizing a powerful kernel extreme learning machine to predict the probability for PPI based on this lowest rank representation. Experimental results on a large-scale human PPI dataset demonstrate that the proposed LELM has significant advantages in accuracy and efficiency over the state-of-art approaches. Hence, this work establishes a new and effective way for the automatic detection of PPI.
AB - Protein-protein interactions (PPIs) play a central role in many biological processes. Although a large amount of human PPI data has been generated by high-throughput experimental techniques, they are very limited compared to the estimated 130 000 protein interactions in humans. Hence, automatic methods for human PPI-detection are highly desired. This work proposes a novel framework, i.e., Low-rank approximation-kernel Extreme Learning Machine (LELM), for detecting human PPI from a protein's primary sequences automatically. It has three main steps: 1) mapping each protein sequence into a matrix built on all kinds of adjacent amino acids; 2) applying the low-rank approximation model to the obtained matrix to solve its lowest rank representation, which reflects its true subspace structures; and 3) utilizing a powerful kernel extreme learning machine to predict the probability for PPI based on this lowest rank representation. Experimental results on a large-scale human PPI dataset demonstrate that the proposed LELM has significant advantages in accuracy and efficiency over the state-of-art approaches. Hence, this work establishes a new and effective way for the automatic detection of PPI.
KW - Big data
KW - feature extraction
KW - kernel extreme learning machine (K-ELM)
KW - low-rank approximation (LRA)
KW - protein-protein interactions (PPIs)
KW - support vector machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=85047726651&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2016.2524994
DO - 10.1109/TCYB.2016.2524994
M3 - 文章
C2 - 28113829
AN - SCOPUS:85047726651
SN - 2168-2267
VL - 47
SP - 731
EP - 743
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 3
M1 - 7444177
ER -