Protein annotation from protein interaction networks and Gene Ontology

Nguyen, Cao; Gardiner, Katheleen; Cios, Krzysztof J.

doi:10.1016/j.jbi.2011.04.010

Cited by 23 publications

(11 citation statements)

References 49 publications

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“…One approach to address the challenge of identifying proteins’ functions is the computational prediction of protein functions ( Radivojac et al , 2013 ). Function prediction can use several sources of information, including protein–protein interactions ( Hou, 2017 ; Jiang and McQuay, 2012 ; Kirac and Ozsoyoglu, 2008 ; Nguyen et al , 2011 ; Sharan et al , 2007 ), genetic interactions ( Costanzo et al , 2016 ), evolutionary relations ( Gaudet et al , 2011 ), protein structures and structure prediction methods ( Konc et al , 2013 ), literature ( Verspoor, 2014 ) or combinations of these ( Sokolov and Ben-Hur, 2010 ). These methods have been developed for many years, and their predictive performance is improving steadily ( Radivojac et al , 2013 ).…”

Section: Introductionmentioning

confidence: 99%

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

2017

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MotivationA large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem.ResultsWe have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.Availability and implementationWeb server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgoSupplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

2017

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“…This information may include secondary structures extracted from input protein sequences, or secondary structure, disordered regions, signal peptides, and motifs like in the case of the FFPred3 method . Finally, several approaches rely on the PPI‐derived information to accurately predict protein functions . The crucial idea behind these methods is that proteins which share similar topological features in the PPI networks may share similar functions .…”

Section: Introductionmentioning

confidence: 99%

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

et al. 2019

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Annotation of protein functions plays an important role in understanding lifeat the molecular level. High-throughput sequencing produces massive numbers of raw proteins sequences and only about 1% of them have been manually annotated with functions. Experimental annotations of functions are expensive, time-consuming and do not keep up with the rapid growth of the sequence numbers. This motivates the development of computational approaches that predict protein functions. A novel deep learning framework, DeepFunc, is proposed which accurately predicts protein functions from protein sequence-and network-derived information. More precisely, DeepFunc uses a long and sparse binary vector to encode information concerning domains, families, and motifs collected from the InterPro tool that is associated with the input protein sequence. This vector is processed with two neural layers to obtain a low-dimensional vector which is combined with topological information extracted from protein-protein interactions (PPIs) and functional linkages. The combined information is processed by a deep neural network that predicts protein functions. DeepFunc is empirically and comparatively tested on a benchmark testing dataset and the Critical Assessment of protein Function Annotation algorithms (CAFA) 3 dataset. The experimental results demonstrate that DeepFunc outperforms current methods on the testing dataset and that it secures the highest F max = 0.54 and AUC = 0.94 on the CAFA3 dataset.

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“…These module assistant methods [5] vary in the methods used to generate function modules. Considering high noise in a PIN data and diversity of protein functions, some researchers have tried to enhance the prediction performance by incorporating other biological information [21], [22], [23], [24], [25], such as gene expression profiles, gene regulatory networks, GO similarity information, homology data, protein complex data, protein domain data and so on, to filter or to weight or to compensate the PIN data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three Biological Networks

Peng

Chen

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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With the gap between the sequence data and their functional annotations becomes increasing wider, many computational methods have been proposed to annotate functions for unknown proteins. However, designing effective methods to make good use of various biological resources is still a big challenge for researchers due to function diversity of proteins. In this work, we propose a new method named ThrRW, which takes several steps of random walking on three different biological networks: protein interaction network (PIN), domain co-occurrence network (DCN), and functional interrelationship network (FIN), respectively, so as to infer functional information from neighbors in the corresponding networks. With respect to the topological and structural differences of the three networks, the number of walking steps in the three networks will be different. In the course of working, the functional information will be transferred from one network to another according to the associations between the nodes in different networks. The results of experiment on S. cerevisiae data show that our method achieves better prediction performance not only than the methods that consider both PIN data and GO term similarities, but also than the methods using both PIN data and protein domain information, which verifies the effectiveness of our method on integrating multiple biological data sources.

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Protein annotation from protein interaction networks and Gene Ontology

Cited by 23 publications

References 49 publications

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions

Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three Biological Networks

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