Integrating network topology, gene expression data and GO annotation information for protein complex prediction

2019 ◽  
Vol 17 (01) ◽  
pp. 1950001 ◽  
Author(s):  
Wei Zhang ◽  
Jia Xu ◽  
Yuanyuan Li ◽  
Xiufen Zou
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Complex ◽  
Complex Disease ◽  
Protein Complexes ◽  
Expression Profiles ◽  
Interaction Network ◽  
Ppi Networks ◽  
Annotation Information

The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict protein complexes based on protein–protein interaction (PPI) networks in recent years. However, because the high throughput data obtained from experimental biotechnology are incomplete, and usually contain a large number of spurious interactions, most of the network-based protein complex identification methods are sensitive to the reliability of the PPI network. In this paper, we propose a new method, Identification of Protein Complex based on Refined Protein Interaction Network (IPC-RPIN), which integrates the topology, gene expression profiles and GO functional annotation information to predict protein complexes from the reconstructed networks. To demonstrate the performance of the IPC-RPIN method, we evaluated the IPC-RPIN on three PPI networks of Saccharomycescerevisiae and compared it with four state-of-the-art methods. The simulation results show that the IPC-RPIN achieved a better result than the other methods on most of the measurements and is able to discover small protein complexes which have traditionally been neglected.

Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles

2019 ◽  
Vol 17 (03) ◽  
pp. 1940007 ◽  
Author(s):  
Teppei Matsubara ◽  
Tomoshiro Ochiai ◽  
Morihiro Hayashida ◽  
Tatsuya Akutsu ◽  
Jose C. Nacher
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Lung Cancer ◽  
Convolutional Neural Network ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Learning Technologies

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to “omics” data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis

scholarly journals Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Min Li ◽  
Weijie Chen ◽  
Jianxin Wang ◽  
Fang-Xiang Wu ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Computational Methods ◽  
Protein Complex ◽  
Protein Complexes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Protein Interaction ◽  
Cellular Life ◽  
Ppi Networks ◽  
Core Proteins

Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data ofSaccharomyces cerevisiaeand the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.

scholarly journals Protein complex prediction based on simultaneous protein interaction network

2009 ◽  
Vol 26 (3) ◽  
pp. 385-391 ◽  
Author(s):  
Suk Hoon Jung ◽  
Bora Hyun ◽  
Woo-Hyuk Jang ◽  
Hee-Young Hur ◽  
Dong-Soo Han
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Complex ◽  
Interaction Network ◽  
Protein Complex Prediction

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

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