K10. Increasing the Reliability of Protein-Protein Interactions and Protein Function Prediction Based on the Experimental Identification Methods

2013 ◽  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Protein Protein Interactions ◽  
Identification Methods ◽  
Experimental Identification

scholarly journals Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

2018 ◽  
Author(s):  
Cen Wan ◽  
Domenico Cozzetto ◽  
Rui Fa ◽  
David T. Jones
Keyword(s):  
Neural Networks ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Functional Representations

Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

scholarly journals ProCbA: Protein Function Prediction based on Clique Analysis

2020 ◽  
Author(s):  
A. Khanteymoori ◽  
M. B. Ghajehlo ◽  
S. Behrouzinia ◽  
M. H. Olyaee
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Experimental Result ◽  
Protein Protein Interactions ◽  
Ppi Networks ◽  
Protein Functions ◽  
Important Challenge ◽  
Result Analysis

AbstractProtein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the Post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for un-annotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods.To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which is not including new algorithm; we try to address the essence of incomplete and noisy data of PPI era in order to achieving a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis as can be seen in Section IV, the both ProCbA and ProNC-FA are generally able to outperform all the other methods.

scholarly journals FunPred 3.0: improved protein function prediction using protein interaction network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6830 ◽  
Author(s):  
Sovan Saha ◽  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Experimental Studies ◽  
Interaction Network ◽  
Function Prediction ◽  
The Self ◽  
Functional Annotations

Proteins are the most versatile macromolecules in living systems and perform crucial biological functions. In the advent of the post-genomic era, the next generation sequencing is done routinely at the population scale for a variety of species. The challenging problem is to massively determine the functions of proteins that are yet not characterized by detailed experimental studies. Identification of protein functions experimentally is a laborious and time-consuming task involving many resources. We therefore propose the automated protein function prediction methodology using in silico algorithms trained on carefully curated experimental datasets. We present the improved protein function prediction tool FunPred 3.0, an extended version of our previous methodology FunPred 2, which exploits neighborhood properties in protein–protein interaction network (PPIN) and physicochemical properties of amino acids. Our method is validated using the available functional annotations in the PPIN network of Saccharomyces cerevisiae in the latest Munich information center for protein (MIPS) dataset. The PPIN data of S. cerevisiae in MIPS dataset includes 4,554 unique proteins in 13,528 protein–protein interactions after the elimination of the self-replicating and the self-interacting protein pairs. Using the developed FunPred 3.0 tool, we are able to achieve the mean precision, the recall and the F-score values of 0.55, 0.82 and 0.66, respectively. FunPred 3.0 is then used to predict the functions of unpredicted protein pairs (incomplete and missing functional annotations) in MIPS dataset of S. cerevisiae. The method is also capable of predicting the subcellular localization of proteins along with its corresponding functions. The code and the complete prediction results are available freely at: https://github.com/SovanSaha/FunPred-3.0.git.

scholarly journals Augmenting protein network embeddings with sequence information

2019 ◽  
Author(s):  
Hassan Kané ◽  
Mohamed Coulibali ◽  
Ali Abdalla ◽  
Pelkins Ajanoh
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Quaternary Structure ◽  
Protein Function Prediction ◽  
Representation Learning ◽  
Specific Protein ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Tissue Specific ◽  
Protein Protein Interaction

ABSTRACTComputational methods that infer the function of proteins are key to understanding life at the molecular level. In recent years, representation learning has emerged as a powerful paradigm to discover new patterns among entities as varied as images, words, speech, molecules. In typical representation learning, there is only one source of data or one level of abstraction at which the learned representation occurs. However, proteins can be described by their primary, secondary, tertiary, and quaternary structure or even as nodes in protein-protein interaction networks. Given that protein function is an emergent property of all these levels of interactions in this work, we learn joint representations from both amino acid sequence and multilayer networks representing tissue-specific protein-protein interactions. Using these hybrid representations, we show that simple machine learning models trained using these hybrid representations outperform existing network-based methods on the task of tissue-specific protein function prediction on 13 out of 13 tissues. Furthermore, these representations outperform existing ones by 14% on average.

scholarly journals TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

2020 ◽  
Author(s):  
Warith Eddine DJEDDI ◽  
Sadok BEN YAHIA ◽  
Engelbert MEPHU NGUIFO
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Direct Interaction ◽  
Function Prediction ◽  
Multiple Alignment ◽  
Protein Protein Interactions ◽  
New Approach ◽  
Ppi Networks ◽  
Phylogenetic Profiles ◽  
Protein Functions

Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactions networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on similar sequence, structure 2D/3D, amino acid motifs or phylogenetic profiles. Results: In this work, we introduce a new approach called TANA for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. Doing so, we are able to discover the functions of proteins that could not to be easily described by sequence homology. We assess the performance of our method using the standard metrics established by the CAFA and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known in advance.

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