Identification of the Disrupted Pathways Associated with Periodontitis Based on Human Pathway Network

AbstractObjective: The objective of this work is to search for a novel method to explore the disrupted pathways associated with periodontitis (PD) based on the network level.Methods: Firstly, the differential expression genes (DEGs) between PD patients and cognitively normal subjects were inferred based on LIMMA package. Then, the protein-protein interactions (PPI) in each pathway were explored by Empirical Bayesian (EB) co-expression program. Specifically, we determined the 100th weight value as the threshold value of the disrupted pathways of PPI by constructing the randomly model and confirmed the weight value of each pathway. Meanwhile, we dissected the disrupted pathways under the weight value > the threshold value. Pathways enrichment analyses of DEGs were carried out based on Expression Analysis Systematic Explored (EASE) test. Finally, the better method was selected based on the more rich and significant obtained pathways by comparing the two methods.Results: After the calculation of LIMMA package, we estimated 524 DEGs in all. Then we determined 0.115222 as the threshold value of the disrupted pathways of PPI. When the weight value>0.115222, there were 258 disrupted pathways of PPI enriched in. Additionally, we observed those 524 DEGs that were enriched in 4 pathways under EASE=0.1.Conclusion: We proposed a novel network method inferring the disrupted pathway for PD. The disrupted pathways might be underlying biomarkers for treatment associated with PD.

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A novel method to map and compare protein-protein interactions in spherical viral capsids

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.22088 ◽

2008 ◽

Vol 73 (3) ◽

pp. 644-655 ◽

Cited By ~ 13

Author(s):

Mauricio Carrillo-Tripp ◽

Charles L. Brooks ◽

Vijay S. Reddy

Keyword(s):

Protein Interactions ◽

Protein Protein Interactions ◽

Viral Capsids ◽

Novel Method

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ChiPPI: a novel method for mapping chimeric protein–protein interactions uncovers selection principles of protein fusion events in cancer

Nucleic Acids Research ◽

10.1093/nar/gkx423 ◽

2017 ◽

Vol 45 (12) ◽

pp. 7094-7105 ◽

Cited By ~ 20

Author(s):

Milana Frenkel-Morgenstern ◽

Alessandro Gorohovski ◽

Somnath Tagore ◽

Vaishnovi Sekar ◽

Miguel Vazquez ◽

...

Keyword(s):

Protein Interactions ◽

Chimeric Protein ◽

Protein Protein Interactions ◽

Protein Fusion ◽

Selection Principles ◽

Novel Method

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Identification of key microRNAs in the carotid arteries of ApoE−/− mice exposed to disturbed flow

Hereditas ◽

10.1186/s41065-019-0112-x ◽

2019 ◽

Vol 156 (1) ◽

Author(s):

Xinzhou Wang ◽

Shuibo Gao ◽

Liping Dai ◽

Zhentao Wang ◽

Hong Wu

Keyword(s):

Blood Flow ◽

Protein Interactions ◽

Carotid Arteries ◽

Target Genes ◽

Pathological Process ◽

Mitogen Activated Protein Kinase ◽

Molecular Pathways ◽

Protein Protein Interactions ◽

Mitogen Activated Protein ◽

Limma Package

Abstract Background Atherosclerosis (AS) is one of the main causes of cardiovascular disease. AS plaques often occur in blood vessels with oscillatory blood flow and their formation can be regulated by microRNAs (miRNAs). The aim of this study is to identify the key miRNAs and molecular pathways involved in this pathological process. Methods In this study, gene chip data obtained from the GEO database was analyzed using the LIMMA package to find differentially expressed miRNAs (DE miRNAs) in the carotid arteries of ApoE−/− mice exposed to different blood flow rates. Predicted targets of the DE miRNAs were identified using the TargetScan, miRDB, and DIANA databases respectively, and the potential target genes (PTGs) were found by analyzing the common results of three databases. The DAVID database was used to enrich the PTGs based on gene ontology (GO) and pathway (Kyoto Encyclopedia of Genes and Genomes, KEGG), and the STRING database was used to uncover any protein-protein interactions (PPI) of the PTGs. Results The networks of the DE miRNAs-PTGs, Pathway-PTGs-DE miRNAs, and PTGs PPI, were constructed using Cytoscape, and 11 up-regulated and 13 down-regulated DE miRNAs and 1479 PTGs were found. GO results showed that PTGs were significantly enriched in functions such as transcriptional regulation and DNA binding. KEGG results showed that PTGs were significantly enriched in inflammation-related mitogen-activated protein kinase (MAPK) and AS-related FOXO pathways. The PPI network revealed some key target genes in the PTGs. Conclusions The analysis of key miRNAs and molecular pathways that regulate the formation of AS plaques induced by oscillatory blood flow will provide new ideas for AS treatment.

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Discovering novel protein–protein interactions by measuring the protein semantic similarity from the biomedical literature

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720014420086 ◽

2014 ◽

Vol 12 (06) ◽

pp. 1442008 ◽

Cited By ~ 4

Author(s):

Jung-Hsien Chiang ◽

Jiun-Huang Ju

Keyword(s):

Semantic Similarity ◽

Protein Interactions ◽

Similarity Measures ◽

Biomedical Literature ◽

Biological Research ◽

Protein Protein Interactions ◽

Automated Identification ◽

Learning Classifier ◽

Novel Method ◽

Novel Protein

Protein–protein interactions (PPIs) are involved in the majority of biological processes. Identification of PPIs is therefore one of the key aims of biological research. Although there are many databases of PPIs, many other unidentified PPIs could be buried in the biomedical literature. Therefore, automated identification of PPIs from biomedical literature repositories could be used to discover otherwise hidden interactions. Search engines, such as Google, have been successfully applied to measure the relatedness among words. Inspired by such approaches, we propose a novel method to identify PPIs through semantic similarity measures among protein mentions. We define six semantic similarity measures as features based on the page counts retrieved from the MEDLINE database. A machine learning classifier, Random Forest, is trained using the above features. The proposed approach achieve an averaged micro-F of 71.28% and an averaged macro-F of 64.03% over five PPI corpora, an improvement over the results of using only the conventional co-occurrence feature (averaged micro-F of 68.79% and an averaged macro-F of 60.49%). A relation-word reinforcement further improves the averaged micro-F to 71.3% and averaged macro-F to 65.12%. Comparing the results of the current work with other studies on the AIMed corpus (ranging from 77.58% to 85.1% in micro-F, 62.18% to 76.27% in macro-F), we show that the proposed approach achieves micro-F of 81.88% and macro-F of 64.01% without the use of sophisticated feature extraction. Finally, we manually examine the newly discovered PPI pairs based on a literature review, and the results suggest that our approach could extract novel protein–protein interactions.

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ACT-SVM: Prediction of Protein-Protein Interactions Based on Support Vector Basis Model

Scientific Programming ◽

10.1155/2020/8866557 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Wenzheng Ma ◽

Yi Cao ◽

Wenzheng Bao ◽

Bin Yang ◽

Yuehui Chen

Keyword(s):

Protein Interactions ◽

Prediction Accuracy ◽

Support Vector ◽

Svm Classifier ◽

Protein Protein Interactions ◽

Svm Model ◽

Novel Method ◽

H Pylori ◽

Almost All ◽

Human Dataset

The interactions between proteins play important roles in several organisms, and such issue can be involved in almost all activities in the cell. The research of protein-protein interactions (PPIs) can make a huge contribution to the prevention and treatment of diseases. Currently, many prediction methods based on machine learning have been proposed to predict PPIs. In this article, we propose a novel method ACT-SVM that can effectively predict PPIs. The ACT-SVM model maps protein sequences to digital features, performs feature extraction twice on the protein sequence to obtain vector A and descriptor CT, and combines them into a vector. Then, the feature vectors of the protein pair are merged as the input of the support vector machine (SVM) classifier. We utilize nonredundant H. pylori and human dataset to verify the prediction performance of our method. Finally, the proposed method has a prediction accuracy of 0.727897 for H. pylori data and a prediction accuracy of 0.838799 for human dataset. The results demonstrate that this method can be called a stable and reliable prediction model of PPIs.

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A novel method based on new adaptive LVQ neural network for predicting protein–protein interactions from protein sequences

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2013.07.001 ◽

2013 ◽

Vol 336 ◽

pp. 231-239 ◽

Cited By ~ 12

Author(s):

Abdulaziz Yousef ◽

Nasrollah Moghadam Charkari

Keyword(s):

Neural Network ◽

Protein Interactions ◽

Protein Sequences ◽

Protein Protein Interactions ◽

Novel Method

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A novel method to predict protein-protein interactions based on the information of protein sequence

2012 IEEE International Conference on Control System, Computing and Engineering ◽

10.1109/iccsce.2012.6487143 ◽

2012 ◽

Cited By ~ 4

Author(s):

Zhu-Hong You ◽

Zhong Ming ◽

Haiyun Huang ◽

Xiaogang Peng

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Protein Protein Interactions ◽

Novel Method

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Combining Semantic Similarity and GO Enrichment for Computation of Functional Similarity

10.1101/155689 ◽

2017 ◽

Author(s):

Wenting Liu ◽

Jianjun Liu ◽

Jagath C. Rajapakse

Keyword(s):

Semantic Similarity ◽

Protein Interactions ◽

Similarity Measures ◽

Functional Similarity ◽

Protein Protein Interactions ◽

Go Enrichment ◽

Sequence Homologies ◽

Benchmark Datasets ◽

Novel Method ◽

Go Terms

AbstractFunctional similarity between genes is widely used in many bioinformatics applications including detecting molecular pathways, finding co-expressed genes, predicting protein-protein interactions, and prioritization of candidate genes. Methods evaluating functional similarity of genes are mostly based on semantic similarity of gene ontology (GO) terms. Though there are hundreds of functional similarity measures available in the literature, none of them considers the enrichment of the GO terms by the querying gene pair. We propose a novel method to incorporate GO enrichment into the existing functional similarity measures. Our experiments show that the inclusion of gene enrichment significantly improves the performance of 44 widely used functional similarity measures, especially in the prediction of sequence homologies, gene expression correlations, and protein-protein interactions.Software availabilityThe software (python code) and all the benchmark datasets evaluation (R script) are available at https://gitlab.com/liuwt/EnrichFunSim.

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