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 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.

scholarly journals Towards extracting supporting information about predicted protein-protein interactions

2015 ◽  
Author(s):  
Adam Roth ◽  
Sandeep Subramanian ◽  
Madhavi Ganapathiraju
Keyword(s):  
Semantic Similarity ◽  
Protein Interactions ◽  
Contextual Information ◽  
Relation Extraction ◽  
Angular Distance ◽  
Biomedical Literature ◽  
Protein Protein Interactions ◽  
Real World Application ◽  
Evidential Support ◽  
Ppi Prediction

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.

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.

Forms and consequences of incompatibility

2019 ◽  
pp. 20-48
Author(s):  
Geoffrey E. Hill
Keyword(s):  
Mitochondrial Dna ◽  
Electron Transport ◽  
Mitochondrial Dysfunction ◽  
Protein Interactions ◽  
Biomedical Literature ◽  
Nuclear Genes ◽  
Extensive Literature ◽  
Protein Protein Interactions ◽  
Growing Body ◽  
Evolutionary Consequences

To understand the evolutionary consequences of poor coadaptation of mitochondrial and nuclear genes, it is necessary to consider in molecular detail the manifestations of mitochondrial dysfunction. Most considerations of mitochondrial dysfunction resulting from mitonuclear incompatibilities focus on protein–protein interactions in the electron transport system, but the interactions of mitochondrial and nuclear genes in enabling the transcription, translation, and replication of mitochondrial DNA can play an equally important role in mitonuclear coevolution and coadaptation. This chapter reviews the extensive literature on how mitochondrial dysfunction is the cause of many inherited human diseases and explains how this biomedical literature connects to a rapidly growing body of research on the evolution and maintenance of coadaptation of mitochondrial and nuclear genes among non-human eukaryotes. The goal of the chapter is to establish the fundamental importance of coadaptation between co-functioning mitochondrial and nuclear genes.

scholarly journals UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Theodosios Theodosiou ◽  
Nikolaos Papanikolaou ◽  
Maria Savvaki ◽  
Giulia Bonetto ◽  
Stella Maxouri ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Computational Prediction ◽  
Biomedical Literature ◽  
Protein Network ◽  
Protein Protein Interactions ◽  
High Coverage ◽  
Depth Study ◽  
Experimental Approaches ◽  
Wet Lab ◽  
User Friendly

Abstract The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

scholarly journals GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Xiaoshi Zhong ◽  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Protein Interactions ◽  
Large Scale ◽  
Functional Similarity ◽  
Experimental Results ◽  
Graph Embeddings ◽  
Protein Protein Interactions ◽  
Vector Representations ◽  
Go Terms

Abstract Background Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions. Results We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures. Conclusion Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins.

scholarly journals HMNPPID—human malignant neoplasm protein–protein interaction database

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Qingqing Li ◽  
Zhihao Yang ◽  
Zhehuan Zhao ◽  
Ling Luo ◽  
Zhiheng Li ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Malignant Neoplasm ◽  
Biomedical Literature ◽  
Malignant Neoplasms ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

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