TY - GEN
T1 - Predicting Protein-Protein Interactions from Protein Sequence Using Locality Preserving Projections and Rotation Forest
AU - Zhan, Xinke
AU - You, Zhuhong
AU - Yu, Changqing
AU - Pan, Jie
AU - Li, Ruiyang
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Protein-protein interactions (PPIs) play an important role in nearly every aspect of the cell function in biological system. A number of high-throughput technologies have been proposed to detect the PPIs in past decades. However, they have some drawbacks such as time-consuming and high cost, and at the same time, a high rate of false positive is also unavoidable. Hence, developing an efficient computational method for predicting PPIs is very necessary and urgent. In this paper, we propose a novel computational method for predicting PPIs from protein sequence using Locality Preserving Projections (LPP) and Rotation Forest (RF) model. Specifically, the protein sequence is firstly transformed into Position Specific Scoring Matrix (PSSM) generated by multiple sequences alignments. Then, the LPP descriptor is applied to extract protein evolutionary information from. Finally, the RF classifier is adopted to predict whether the given protein pair is interacting or not. When the proposed method performed on Yeast and H. pylori PPIs datasets, it achieved the results with an average accuracy of 92.52% and 91.46%, respectively. To further verify the performance of the proposed method, we compare the proposed method with the state-of-the-art support vector machine (SVM) and get good results. The promising results indicated the proposed method is stable and robust for predicting PPIs.
AB - Protein-protein interactions (PPIs) play an important role in nearly every aspect of the cell function in biological system. A number of high-throughput technologies have been proposed to detect the PPIs in past decades. However, they have some drawbacks such as time-consuming and high cost, and at the same time, a high rate of false positive is also unavoidable. Hence, developing an efficient computational method for predicting PPIs is very necessary and urgent. In this paper, we propose a novel computational method for predicting PPIs from protein sequence using Locality Preserving Projections (LPP) and Rotation Forest (RF) model. Specifically, the protein sequence is firstly transformed into Position Specific Scoring Matrix (PSSM) generated by multiple sequences alignments. Then, the LPP descriptor is applied to extract protein evolutionary information from. Finally, the RF classifier is adopted to predict whether the given protein pair is interacting or not. When the proposed method performed on Yeast and H. pylori PPIs datasets, it achieved the results with an average accuracy of 92.52% and 91.46%, respectively. To further verify the performance of the proposed method, we compare the proposed method with the state-of-the-art support vector machine (SVM) and get good results. The promising results indicated the proposed method is stable and robust for predicting PPIs.
KW - Locality preserving projections
KW - PSSM
KW - Rotation forest
UR - http://www.scopus.com/inward/record.url?scp=85094165892&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60802-6_12
DO - 10.1007/978-3-030-60802-6_12
M3 - 会议稿件
AN - SCOPUS:85094165892
SN - 9783030608019
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 121
EP - 131
BT - Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings
A2 - Huang, De-Shuang
A2 - Jo, Kang-Hyun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Intelligent Computing, ICIC 2020
Y2 - 2 October 2020 through 5 October 2020
ER -