scholarly journals A large-scale evaluation of computational protein function prediction

2013 ◽  
Vol 10 (3) ◽  
pp. 221-227 ◽  
Author(s):  
Predrag Radivojac ◽  
Wyatt T Clark ◽  
Tal Ronnen Oron ◽  
Alexandra M Schnoes ◽  
Tobias Wittkop ◽  
...  
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Scale Evaluation

scholarly journals deepNF: Deep network fusion for protein function prediction

2017 ◽  
Author(s):  
Vladimir Gligorijević ◽  
Meet Barot ◽  
Richard Bonneau
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Predictive Performance ◽  
Substantial Improvement ◽  
Function Prediction ◽  
Interaction Networks ◽  
Highly Nonlinear ◽  
High Level ◽  
String Networks

AbstractThe prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provide a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that cannot capture complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity.AvailabilitydeepNF is freely available at: https://github.com/VGligorijevic/deepNF

scholarly journals A look back at the quality of Protein Function Prediction tools in CAFA

2018 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Madhusudan Srinivasan ◽  
Diane Bimczok ◽  
Upulee Kanewala ◽  
Indika Kahanda
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Computational Models ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Test Case ◽  
Test Cases ◽  
Metamorphic Testing ◽  
Main Challenge ◽  
Scale Experiment

The Critical Assessment of protein Function Annotation algorithms (CAFA) is a large-scale experiment for assessing the computational models for automated function prediction (AFP). The models presented in CAFA have shown excellent promise in terms of prediction accuracy, but quality assurance has been paid relatively less attention. The main challenge associated with conducting systematic testing on AFP software is the lack of a test oracle, which determines passing or failing of a test case; unfortunately, the exact expected outcomes are not well defined for the AFP task. Thus, AFP tools face the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem using metamorphic relations (MRs). A MR determines whether a test has passed or failed by specifying how the output should change according to a specific change made to the input. In this work, we use MT to test nine CAFA2 AFP tools by defining a set of MRs that apply input transformations at the protein-level. According to our initial testing, we observe that several tools fail all the test cases and two tools pass all the test cases on different GO ontologies.

scholarly journals Large-scale protein function prediction using heterogeneous ensembles

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1577 ◽  
Author(s):  
Linhua Wang ◽  
Jeffrey Law ◽  
Shiv D. Kale ◽  
T. M. Murali ◽  
Gaurav Pandey
Keyword(s):  
Gene Ontology ◽  
Logistic Regression ◽  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Ensemble Methods ◽  
Function Prediction ◽  
Data Type ◽  
Heterogeneous Ensemble ◽  
The Ideal

Heterogeneous ensembles are an effective approach in scenarios where the ideal data type and/or individual predictor are unclear for a given problem. These ensembles have shown promise for protein function prediction (PFP), but their ability to improve PFP at a large scale is unclear. The overall goal of this study is to critically assess this ability of a variety of heterogeneous ensemble methods across a multitude of functional terms, proteins and organisms. Our results show that these methods, especially Stacking using Logistic Regression, indeed produce more accurate predictions for a variety of Gene Ontology terms differing in size and specificity. To enable the application of these methods to other related problems, we have publicly shared the HPC-enabled code underlying this work as LargeGOPred (https://github.com/GauravPandeyLab/LargeGOPred).

scholarly journals GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank

2018 ◽  
Vol 34 (14) ◽  
pp. 2465-2473 ◽  
Author(s):  
Ronghui You ◽  
Zihan Zhang ◽  
Yi Xiong ◽  
Fengzhu Sun ◽  
Hiroshi Mamitsuka ◽  
...  
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Learning To Rank ◽  
Function Prediction

scholarly journals Wei2GO: weighted sequence similarity-based protein function prediction

2020 ◽  
Author(s):  
Maarten J.M.F Reijnders
Keyword(s):  
Gene Ontology ◽  
Open Source ◽  
Protein Function ◽  
Large Scale ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Computational Time ◽  
Web Servers ◽  
Weighted Sequence

AbstractBackgroundProtein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the presence of limitations imposed by web servers such as queues, prediction speed, and updatability of databases.MethodsThis paper describes Wei2GO: a weighted sequence similarity and python-based open-source protein function prediction software. It uses DIAMOND and HMMScan sequence alignment searches against the UniProtKB and Pfam databases respectively, transfers Gene Ontology terms from the reference protein to the query protein, and uses a weighing algorithm to calculate a score for the Gene Ontology annotations.ResultsWei2GO is compared against the Argot2 and Argot2.5 web servers, which use a similar concept, and DeepGOPlus which acts as a reference. Wei2GO shows an increase in performance according to precision and recall curves, Fmax scores, and Smin scores for biological process and molecular function ontologies. Computational time compared to Argot2 and Argot2.5 is decreased from several hours to several minutes.AvailabilityWei2GO is written in Python 3, and can be found at https://gitlab.com/mreijnders/Wei2GO

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 A look back at the quality of Protein Function Prediction tools in CAFA

2018 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Madhusudan Srinivasan ◽  
Diane Bimczok ◽  
Upulee Kanewala ◽  
Indika Kahanda
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Computational Models ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Test Case ◽  
Test Cases ◽  
Metamorphic Testing ◽  
Main Challenge ◽  
Scale Experiment

The Critical Assessment of protein Function Annotation algorithms (CAFA) is a large-scale experiment for assessing the computational models for automated function prediction (AFP). The models presented in CAFA have shown excellent promise in terms of prediction accuracy, but quality assurance has been paid relatively less attention. The main challenge associated with conducting systematic testing on AFP software is the lack of a test oracle, which determines passing or failing of a test case; unfortunately, the exact expected outcomes are not well defined for the AFP task. Thus, AFP tools face the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem using metamorphic relations (MRs). A MR determines whether a test has passed or failed by specifying how the output should change according to a specific change made to the input. In this work, we use MT to test nine CAFA2 AFP tools by defining a set of MRs that apply input transformations at the protein-level. According to our initial testing, we observe that several tools fail all the test cases and two tools pass all the test cases on different GO ontologies.

scholarly journals NetGO: improving large-scale protein function prediction with massive network information

2019 ◽  
Vol 47 (W1) ◽  
pp. W379-W387 ◽  
Author(s):  
Ronghui You ◽  
Shuwei Yao ◽  
Yi Xiong ◽  
Xiaodi Huang ◽  
Fengzhu Sun ◽  
...  
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Learning To Rank ◽  
Web Server ◽  
Function Prediction ◽  
Multiple Gene ◽  
Network Information ◽  
Powerful Learning ◽  
Specific Species

Abstract Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Based on our GOLabeler—a state-of-the-art method for the third critical assessment of functional annotation (CAFA3), in this paper we propose NetGO, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information. Specifically, the advantages of NetGO are threefold in using network information: (i) NetGO relies on a powerful learning to rank framework from machine learning to effectively integrate both sequence and network information of proteins; (ii) NetGO uses the massive network information of all species (>2000) in STRING (other than only some specific species) and (iii) NetGO still can use network information to annotate a protein by homology transfer, even if it is not contained in STRING. Separating training and testing data with the same time-delayed settings of CAFA, we comprehensively examined the performance of NetGO. Experimental results have clearly demonstrated that NetGO significantly outperforms GOLabeler and other competing methods. The NetGO web server is freely available at http://issubmission.sjtu.edu.cn/netgo/.

scholarly journals GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank

2017 ◽  
Author(s):  
Ronghui You ◽  
Zihan Zhang ◽  
Yi Xiong ◽  
Fengzhu Sun ◽  
Hiroshi Mamitsuka ◽  
...  
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Learning To Rank ◽  
Classification Problem ◽  
Function Prediction ◽  
New Paradigm ◽  
Effective Manner ◽  
Homology Information ◽  
Significant Performance

AbstractMotivation: Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only ¡1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multi-label classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore, homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-calleddifficultproteins, which have ¡60% sequence identity to proteins with annotations already. Thus, the vital and challenging problem now is to develop a method for SAFP, particularly for difficult proteins.Methods: The key of this method is to extract not only homology information but also diverse, deep-rooted information/evidence from sequence inputs and integrate them into a predictor in an efficient and also effective manner. We propose GOLabeler, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a new paradigm of machine learning, especially powerful for multi-label classification.Results: The empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP methods.Contact:[email protected]

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