scholarly journals Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

2016 ◽  
Vol 25 (10) ◽  
pp. 1825-1833 ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Xing Chen ◽  
Gui-Ying Yan ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Prediction Accuracy ◽  
Relevance Vector Machine ◽  
Evolutionary Information ◽  
Protein Protein Interactions ◽  
Machine Model

scholarly journals ALPINE: Active Link Prediction Using Network Embedding

2021 ◽  
Vol 11 (11) ◽  
pp. 5043
Author(s):  
Xi Chen ◽  
Bo Kang ◽  
Jefrey Lijffijt ◽  
Tijl De Bie
Keyword(s):  
Active Learning ◽  
Protein Interactions ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Real Data ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Additional Information ◽  
The Cost ◽  
Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

Inference of Protein-Protein Interactions by Using Co-evolutionary Information

2007 ◽  
pp. 322-333
Author(s):  
Tetsuya Sato ◽  
Yoshihiro Yamanishi ◽  
Katsuhisa Horimoto ◽  
Minoru Kanehisa ◽  
Hiroyuki Toh
Keyword(s):  
Protein Interactions ◽  
Evolutionary Information ◽  
Protein Protein Interactions

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.

scholarly journals mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

2019 ◽  
Vol 47 (W1) ◽  
pp. W338-W344 ◽  
Author(s):  
Carlos H M Rodrigues ◽  
Yoochan Myung ◽  
Douglas E V Pires ◽  
David B Ascher
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Evolutionary Information ◽  
Biological Processes ◽  
Missense Mutations ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Residue Interaction ◽  
User Friendly ◽  
Network Metrics

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

scholarly journals Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mayumi Kamada ◽  
Yusuke Sakuma ◽  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interactions ◽  
Interaction Strength ◽  
Feature Space ◽  
Relevance Vector Machine ◽  
Protein Domain ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Living Organisms ◽  
Domain Information

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.

scholarly journals Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

2012 ◽  
Vol 2012 ◽  
pp. 1-23
Author(s):  
J. M. Urquiza ◽  
I. Rojas ◽  
H. Pomares ◽  
J. Herrera ◽  
J. P. Florido ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hierarchical Clustering ◽  
Protein Interactions ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Machine Model ◽  
Cellular Processes ◽  
Parallel Feature ◽  
Ppi Prediction

Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.

scholarly journals Prediction of Protein-Protein Interactions Based On Weighted Extreme Learning Machine And Speed Up Robot Features

2021 ◽  
Author(s):  
JinXuan Zhai ◽  
Ji-Yong An
Keyword(s):  
Extreme Learning Machine ◽  
Protein Interactions ◽  
Evolutionary Information ◽  
Protein Protein Interactions ◽  
Prediction Ability ◽  
Great Ability ◽  
Weighted Extreme Learning Machine ◽  
Comparison Results ◽  
Speed Up ◽  
Learning Machine

Abstract Background：Protein–protein interactions (PPIs) are involved in a number of cellular processes and play a key role inside cells. The prediction of PPIs is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. Given that high-throughput methods are expensive and time-consuming, it is a challenging task to develop efficient and accurate computational methods for predicting PPIs .Results：In the study, a novel computational approach named WELM-SURF was developed to predict PPIs. The proposed method used Position Specific Scoring Matrix (PSSM) to capture protein evolutionary information and employed Speed Up Robot Features (SURF) to extract key features from PSSM of protein sequence. Weighted Extreme Learning Machine (WELM) is featured with short training time and great ability to execute classification efficiently by optimizing the loss function of weight matrix. Therefore, WELM classifier was used to carry out classification. The cross-validation results show that WELM-SURF obtains 97.36% and 95.12% of average accuracy on yeast and human dataset, respectively. The prediction ability of WELM-SURF was also compared with those of ELM-SRUF, SVM-SURF and other existing approaches. The comparison results further verify that WELM-SURF is obviously better than other methods.Conclusion：The experimental results proved that the WELM-SURF method is very useful for predicting PPIs and can also be applied to other bioinformatics studies of protein.

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