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

2008 ◽  
Vol 73 (3) ◽  
pp. 644-655 ◽  
Author(s):  
Mauricio Carrillo-Tripp ◽  
Charles L. Brooks ◽  
Vijay S. Reddy
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Viral Capsids ◽  
Novel Method

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

2016 ◽  
Vol 5 (4) ◽  
pp. 93-98
Author(s):  
Wen Sun ◽  
Lin Han ◽  
Wenmao Xu ◽  
Yazhen Sun
Keyword(s):  
Protein Interactions ◽  
Threshold Value ◽  
Normal Subjects ◽  
Protein Protein Interactions ◽  
Pathway Network ◽  
Differential Expression Genes ◽  
Weight Value ◽  
Limma Package ◽  
Novel Method ◽  
Cognitively Normal Subjects

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.

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

2017 ◽  
Vol 45 (12) ◽  
pp. 7094-7105 ◽  
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

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

2014 ◽  
Vol 12 (06) ◽  
pp. 1442008 ◽  
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.

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

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.

scholarly journals Combining Semantic Similarity and GO Enrichment for Computation of Functional Similarity

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.

Predicting protein–protein interactions between human and hepatitis C virus via an ensemble learning method

2014 ◽  
Vol 10 (12) ◽  
pp. 3147-3154 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Bahram Goliaei ◽  
Javad Zahiri ◽  
Reza Ebrahimpour
Keyword(s):  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Ensemble Learning ◽  
Protein Interactions ◽  
Learning Method ◽  
Protein Protein Interactions ◽  
Novel Method ◽  
Comprehensive Study

We developed a novel method to predict human–HCV protein–protein interactions, the most comprehensive study of this type.

scholarly journals Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2

2020 ◽  
Author(s):  
Y-H. Taguchi ◽  
Turki Turki
Keyword(s):  
Protein Interactions ◽  
Hepatitis Virus ◽  
Tensor Decomposition ◽  
Suitable Model ◽  
The Novel ◽  
Protein Protein Interactions ◽  
Altered Expression ◽  
Novel Method ◽  
Coronavirus Infection ◽  
Novel Coronavirus

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an \textit{in silico} method to identify candidate drugs for treating COVID-19.

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