Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

Gene multifunctionality scoring using gene ontology

2018 ◽  
Vol 16 (05) ◽  
pp. 1840018 ◽  
Author(s):  
Hisham Al-Mubaid
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Evaluation Criteria ◽  
Computational Method ◽  
Protein Protein Interactions ◽  
Validity And Reliability ◽  
Functional Annotations ◽  
Gene Sets ◽  
Human Genes ◽  
Disease Associations

Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional. The proposed method can be employed to identify multifunctional genes in the entire human genome using solely the GO annotations. We evaluated the proposed method in scoring multifunctionality of all human genes using four criteria: gene-disease associations; protein–protein interactions; gene studies with PubMed publications; and published known multifunctional gene sets. The evaluation results confirm the validity and reliability of the proposed method for identifying multifunctional human genes. The results across all four evaluation criteria were statistically significant in determining multifunctionality. For example, the method confirmed that multifunctional genes tend to be associated with diseases more than other genes, with significance [Formula: see text]. Moreover, consistent with all previous studies, proteins encoded by multifunctional genes, based on our method, are involved in protein–protein interactions significantly more ([Formula: see text]) than other proteins.

scholarly journals Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yu-An Huang ◽  
Zhu-Hong You ◽  
Xin Gao ◽  
Leon Wong ◽  
Lirong Wang
Keyword(s):  
Sparse Representation ◽  
Protein Interactions ◽  
Protein Sequence ◽  
False Positive Rate ◽  
Computational Method ◽  
Substitution Matrix ◽  
Support Vector ◽  
Svm Classifier ◽  
Protein Protein Interactions ◽  
Discrete Cosine Transformation

Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset ofYeast,Human, andH. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we usedYeastPPIs samples as training set to predict PPIs of other five species datasets.

scholarly journals Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin He ◽  
Linai Kuang ◽  
Zhiping Chen ◽  
Yihong Tan ◽  
Lei Wang
Keyword(s):  
Protein Interactions ◽  
Computational Models ◽  
Prediction Method ◽  
Computational Method ◽  
Protein Domain ◽  
Biological Information ◽  
Protein Protein Interactions ◽  
Pagerank Algorithm ◽  
Essential Proteins ◽  
Novel Protein

In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.

scholarly journals Ontology-based validation and identification of regulatory phenotypes

2018 ◽  
Author(s):  
Maxat Kulmanov ◽  
Paul N Schofield ◽  
Georgios V Gkoutos ◽  
Robert Hoehndorf
Keyword(s):  
Protein Interactions ◽  
Research Group ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Computational Method ◽  
Gene Products ◽  
Protein Protein Interactions ◽  
Functional Annotations ◽  
Disease Associations ◽  
Annotation Quality

AbstractMotivationFunction annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations.ResultsWe developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

INFERRING PROTEIN-PROTEIN INTERACTIONS FROM MESSENGER RNA EXPRESSION PROFILES WITH SVM

2005 ◽  
Vol 13 (03) ◽  
pp. 287-298 ◽  
Author(s):  
JUN CAI ◽  
YING HUANG ◽  
LIANG JI ◽  
YANDA LI
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
Messenger Rna ◽  
Expression Profiles ◽  
Support Vector ◽  
Svm Classifier ◽  
Good Prediction ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
High Throughput Experiments

In post-genomic biology, researchers in the field of proteome focus their attention on the networks of protein interactions that control the lives of cells and organisms. Protein-protein interactions play a useful role in dynamic cellular machinery. In this paper, we developed a method to infer protein-protein interactions based on the theory of support vector machine (SVM). For a given pair of proteins, a new strategy of calculating cross-correlation function of mRNA expression profiles was used to encode SVM vectors. We compared the performance with other methods of inferring protein-protein interaction. Results suggested that, through five-fold cross validation, our SVM model achieved a good prediction. It enables us to show that expression profiles in transcription level can be used to distinguish physical or functional interactions of proteins as well as sequence contents. Lastly, we applied our SVM classifier to evaluate data quality of interaction data sets from four high-throughput experiments. The results show that high-throughput experiments sacrifice some accuracy in determination of interactions because of limitation of experiment technologies.

scholarly journals Environmental and Genetic Factors in Autism Spectrum Disorders: Special Emphasis on Data from Arabian Studies

2019 ◽  
Vol 16 (4) ◽  
pp. 658 ◽  
Author(s):  
Noor Almandil ◽  
Deem Alkuroud ◽  
Sayed AbdulAzeez ◽  
Abdulla AlSulaiman ◽  
Abdelhamid Elaissari ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Genetic Factors ◽  
Treatment Strategies ◽  
Autism Spectrum ◽  
Pharmacological Treatments ◽  
Protein Protein Interactions ◽  
Improve Treatment ◽  
Comprehensive Survey ◽  
Patterns Of Behavior ◽  
Spectrum Disorders

One of the most common neurodevelopmental disorders worldwide is autism spectrum disorder (ASD), which is characterized by language delay, impaired communication interactions, and repetitive patterns of behavior caused by environmental and genetic factors. This review aims to provide a comprehensive survey of recently published literature on ASD and especially novel insights into excitatory synaptic transmission. Even though numerous genes have been discovered that play roles in ASD, a good understanding of the pathophysiologic process of ASD is still lacking. The protein–protein interactions between the products of NLGN, SHANK, and NRXN synaptic genes indicate that the dysfunction in synaptic plasticity could be one reason for the development of ASD. Designing more accurate diagnostic tests for the early diagnosis of ASD would improve treatment strategies and could enhance the appropriate monitoring of prognosis. This comprehensive review describes the psychotropic and antiepileptic drugs that are currently available as effective pharmacological treatments and provides in-depth knowledge on the concepts related to clinical, diagnostic, therapeutic, and genetic perspectives of ASD. An increase in the prevalence of ASD in Gulf Cooperation Council countries is also addressed in the review. Further, the review emphasizes the need for international networking and multidimensional studies to design novel and effective treatment strategies.

scholarly journals Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Li ◽  
Zheng Wang ◽  
Li-Ping Li ◽  
Zhu-Hong You ◽  
Wen-Zhun Huang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Large Scale ◽  
False Positive Rate ◽  
Computational Method ◽  
Evolutionary Information ◽  
Local Alignment ◽  
Protein Interaction Data ◽  
Sequence Information ◽  
Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

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