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Wang W, Shuai Y, Yang Q, Zhang F, Zeng M, Li M. A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches. Brief Bioinform 2024; 25:bbae050. [PMID: 38388682 PMCID: PMC10883809 DOI: 10.1093/bib/bbae050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/17/2024] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF.
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Affiliation(s)
- Wenkang Wang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Yunyan Shuai
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Qiurong Yang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
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Woicik A, Zhang M, Xu H, Mostafavi S, Wang S. Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling. Bioinformatics 2023; 39:i504-i512. [PMID: 37387142 DOI: 10.1093/bioinformatics/btad247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks. RESULTS To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini's performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings' performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains. AVAILABILITY AND IMPLEMENTATION Gemini can be accessed at: https://github.com/MinxZ/Gemini.
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Affiliation(s)
- Addie Woicik
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, United States
| | - Mingxin Zhang
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, United States
| | - Hanwen Xu
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, United States
| | - Sara Mostafavi
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, United States
| | - Sheng Wang
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, United States
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Li M, Shi W, Zhang F, Zeng M, Li Y. A Deep Learning Framework for Predicting Protein Functions With Co-Occurrence of GO Terms. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:833-842. [PMID: 35476573 DOI: 10.1109/tcbb.2022.3170719] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The understanding of protein functions is critical to many biological problems such as the development of new drugs and new crops. To reduce the huge gap between the increase of protein sequences and annotations of protein functions, many methods have been proposed to deal with this problem. These methods use Gene Ontology (GO) to classify the functions of proteins and consider one GO term as a class label. However, they ignore the co-occurrence of GO terms that is helpful for protein function prediction. We propose a new deep learning model, named DeepPFP-CO, which uses Graph Convolutional Network (GCN) to explore and capture the co-occurrence of GO terms to improve the protein function prediction performance. In this way, we can further deduce the protein functions by fusing the predicted propensity of the center function and its co-occurrence functions. We use Fmax and AUPR to evaluate the performance of DeepPFP-CO and compare DeepPFP-CO with state-of-the-art methods such as DeepGOPlus and DeepGOA. The computational results show that DeepPFP-CO outperforms DeepGOPlus and other methods. Moreover, we further analyze our model at the protein level. The results have demonstrated that DeepPFP-CO improves the performance of protein function prediction. DeepPFP-CO is available at https://csuligroup.com/DeepPFP/.
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Hu S, Luo Y, Zhang Z, Xiong H, Yan W, Jiang M, Zhao B. Protein function annotation based on heterogeneous biological networks. BMC Bioinformatics 2022; 23:493. [DOI: 10.1186/s12859-022-05057-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
Abstract
Background
Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data.
Results
In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively.
Conclusions
We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.
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Mansoor M, Nauman M, Rehman HU, Omar M. Gene Ontology Capsule GAN: an improved architecture for protein function prediction. PeerJ Comput Sci 2022; 8:e1014. [PMID: 36092003 PMCID: PMC9454774 DOI: 10.7717/peerj-cs.1014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the core of all functions pertaining to living things. They consist of an extended amino acid chain folding into a three-dimensional shape that dictates their behavior. Currently, convolutional neural networks (CNNs) have been pivotal in predicting protein functions based on protein sequences. While it is a technology crucial to the niche, the computation cost and translational invariance associated with CNN make it impossible to detect spatial hierarchies between complex and simpler objects. Therefore, this research utilizes capsule networks to capture spatial information as opposed to CNNs. Since capsule networks focus on hierarchical links, they have a lot of potential for solving structural biology challenges. In comparison to the standard CNNs, our results exhibit an improvement in accuracy. Gene Ontology Capsule GAN (GOCAPGAN) achieved an F1 score of 82.6%, a precision score of 90.4% and recall score of 76.1%.
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Hu S, Zhang Z, Xiong H, Jiang M, Luo Y, Yan W, Zhao B. A tensor-based bi-random walks model for protein function prediction. BMC Bioinformatics 2022; 23:199. [PMID: 35637427 PMCID: PMC9150346 DOI: 10.1186/s12859-022-04747-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/24/2022] [Indexed: 11/26/2022] Open
Abstract
Background The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein–protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. Results In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. Conclusions The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04747-2.
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Affiliation(s)
- Sai Hu
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
| | - Zhihong Zhang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Hunan Provincial Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, Hunan, China
| | - Huijun Xiong
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
| | - Meiping Jiang
- Department of Ultrasound, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.,NHC Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital), Changsha, 410100, Hunan, China
| | - Yingchun Luo
- Department of Ultrasound, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.,NHC Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Health Care Hospital), Changsha, 410100, Hunan, China
| | - Wei Yan
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
| | - Bihai Zhao
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China. .,Hunan Provincial Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, 410022, Hunan, China.
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Zhang F, Song H, Zeng M, Wu FX, Li Y, Pan Y, Li M. A Deep Learning Framework for Gene Ontology Annotations With Sequence- and Network-Based Information. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2208-2217. [PMID: 31985440 DOI: 10.1109/tcbb.2020.2968882] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Knowledge of protein functions plays an important role in biology and medicine. With the rapid development of high-throughput technologies, a huge number of proteins have been discovered. However, there are a great number of proteins without functional annotations. A protein usually has multiple functions and some functions or biological processes require interactions of a plurality of proteins. Additionally, Gene Ontology provides a useful classification for protein functions and contains more than 40,000 terms. We propose a deep learning framework called DeepGOA to predict protein functions with protein sequences and protein-protein interaction (PPI) networks. For protein sequences, we extract two types of information: sequence semantic information and subsequence-based features. We use the word2vec technique to numerically represent protein sequences, and utilize a Bi-directional Long and Short Time Memory (Bi-LSTM) and multi-scale convolutional neural network (multi-scale CNN) to obtain the global and local semantic features of protein sequences, respectively. Additionally, we use the InterPro tool to scan protein sequences for extracting subsequence-based information, such as domains and motifs. Then, the information is plugged into a neural network to generate high-quality features. For the PPI network, the Deepwalk algorithm is applied to generate its embedding information of PPI. Then the two types of features are concatenated together to predict protein functions. To evaluate the performance of DeepGOA, several different evaluation methods and metrics are utilized. The experimental results show that DeepGOA outperforms DeepGO and BLAST.
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Zhao Y, Wang J, Guo M, Zhang X, Yu G. Cross-Species Protein Function Prediction with Asynchronous-Random Walk. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1439-1450. [PMID: 31562099 DOI: 10.1109/tcbb.2019.2943342] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein function prediction is a fundamental task in the post-genomic era. Available functional annotations of proteins are incomplete and the annotations of two homologous species are complementary to each other. However, how to effectively leverage mutually complementary annotations of different species to further boost the prediction performance is still not well studied. In this paper, we propose a cross-species protein function prediction approach by performing Asynchronous Random Walk on a heterogeneous network (AsyRW). AsyRW first constructs a heterogeneous network to integrate multiple functional association networks derived from different biological data, established homology-relationships between proteins from different species, known annotations of proteins and Gene Ontology (GO). To account for the intrinsic structures of intra- and inter-species of proteins and that of GO, AsyRW quantifies the individual walk lengths of each network node using the gravity-like theory, and then performs asynchronous-random walk with the individual length to predict associations between proteins and GO terms. Experiments on annotations archived in different years show that individual walk length and asynchronous-random walk can effectively leverage the complementary annotations of different species, AsyRW has a significantly improved performance to other related and competitive methods. The codes of AsyRW are available at: http://mlda.swu.edu.cn/codes.php?name=AsyRW.
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9
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Wang C, Sun K, Wang J, Guo M. Data fusion-based algorithm for predicting miRNA–Disease associations. Comput Biol Chem 2020; 88:107357. [DOI: 10.1016/j.compbiolchem.2020.107357] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 07/24/2020] [Accepted: 08/05/2020] [Indexed: 11/30/2022]
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NPF:network propagation for protein function prediction. BMC Bioinformatics 2020; 21:355. [PMID: 32787776 PMCID: PMC7430911 DOI: 10.1186/s12859-020-03663-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 07/14/2020] [Indexed: 11/29/2022] Open
Abstract
Background The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. Results We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. Conclusions We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.
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11
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Song J, Peng W, Wang F. An Entropy-Based Method for Identifying Mutual Exclusive Driver Genes in Cancer. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:758-768. [PMID: 30763245 DOI: 10.1109/tcbb.2019.2897931] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Cancer in essence is a complex genomic alteration disease which is caused by the somatic mutations during the lifetime. According to previous researches, the first step to overcome cancer is to identify driver genes which can promote carcinogenesis. However, it is still a big challenge to precisely and efficiently extract the cancer related driver genes because the nature of cancer is heterogeneous and there exists tremendously irrelevant passenger mutations which have no function impact on the cancer's development. In this work, we proposed a novel entropy-based method namely EntroRank to identify driver genes by integrating the subcellular localization information and mutual exclusive of variation frequency into the network. EntroRank can take into full consideration different properties of driver genes. Considering the modularity of driver genes, the mutated genes in the network were first clustered into different subgroups according to their located compartments. After that, the structural entropy of the gene in the subgroup was employed to measure its indispensability. Considering mutual exclusive property between driver genes in the modules, relative entropy was utilized to measure the degree of mutual exclusive between two mutated genes in terms of their variation frequency. We applied our method to three different cancers including lung, prostate, and breast cancer. The results show our method not only detect the well-known important drivers but also prioritiz the rare unknown driver genes. Besides, EntroRank can identify driver genes having mutual exclusive property. Compared with other existing methods, our method achieves a better performance for most of cancer types in terms of Precision, Recall, and Fscore.
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Li M, Meng X, Zheng R, Wu FX, Li Y, Pan Y, Wang J. Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:817-827. [PMID: 28885159 DOI: 10.1109/tcbb.2017.2749571] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.
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Liu F, Peng L, Tian G, Yang J, Chen H, Hu Q, Liu X, Zhou L. Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk. Front Bioeng Biotechnol 2020; 8:131. [PMID: 32258003 PMCID: PMC7090022 DOI: 10.3389/fbioe.2020.00131] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/10/2020] [Indexed: 12/05/2022] Open
Abstract
Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.
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Affiliation(s)
- Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing, China
| | | | - Hui Chen
- College of Chemical Engineering, Xiangtan University, Xiangtan, China
| | - Qi Hu
- Xiangya Second Hospital, Central South University, Changsha, Hunan, China
| | - Xiaojun Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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Peng L, Liu F, Yang J, Liu X, Meng Y, Deng X, Peng C, Tian G, Zhou L. Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms. Front Genet 2020; 10:1346. [PMID: 32082358 PMCID: PMC7005249 DOI: 10.3389/fgene.2019.01346] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/09/2019] [Indexed: 12/31/2022] Open
Abstract
Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Jialiang Yang
- Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China
| | - Xiaojun Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiaojun Deng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Cheng Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification. Genes (Basel) 2020; 11:genes11020153. [PMID: 32023848 PMCID: PMC7074227 DOI: 10.3390/genes11020153] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/27/2020] [Accepted: 01/29/2020] [Indexed: 11/18/2022] Open
Abstract
Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein–protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes’ sequence information or genes’ centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches.
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Song J, Peng W, Wang F, Wang J. Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network. BMC Med Genomics 2019; 12:168. [PMID: 31888619 PMCID: PMC6936147 DOI: 10.1186/s12920-019-0619-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 11/11/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Cancer as a kind of genomic alteration disease each year deprives many people's life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance. METHODS Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN). RESULTS This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes. CONCLUSION The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.
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Affiliation(s)
- Junrong Song
- Faculty of Management and Economics/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People's Republic of China
| | - Wei Peng
- Faculty of Management and Economics/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People's Republic of China.
| | - Feng Wang
- Faculty of Management and Economics/Faculty of Information Engineering and Automation/Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People's Republic of China
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, People's Republic of China
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17
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Zhang D, Kabuka M. Multimodal deep representation learning for protein interaction identification and protein family classification. BMC Bioinformatics 2019; 20:531. [PMID: 31787089 PMCID: PMC6886253 DOI: 10.1186/s12859-019-3084-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. RESULTS In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. CONCLUSION To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.
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Affiliation(s)
- Da Zhang
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, U.S..
| | - Mansur Kabuka
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, U.S
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Jiang J, Xing F, Zeng X, Zou Q. Investigating Maize Yield-Related Genes in Multiple Omics Interaction Network Data. IEEE Trans Nanobioscience 2019; 19:142-151. [PMID: 31170079 DOI: 10.1109/tnb.2019.2920419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Zea mays (maize) is the highest yielding food crop globally, feeding large numbers of people across the planet. It is thus especially important to explore the key genes that affect maize production with prior knowledge. Merging multiple datasets of different types can improve the accuracy of candidate genes prediction results, so we constructed interaction networks using gene, mRNA, protein, and expression profile datasets. A network propagation schedule was used considering combined scores obtained by integrating both network scores and significance scores for each candidate gene based on the guilt-by-association principle. An SVM model was used to optimize the weighted parameters to achieve more reliable results, according to the accuracy of label classification. We found that integrating multiple omics data with more data types improves the reliability of the results. We investigated the GO terms particularly associated with the top 100 candidate genes and the known genes, and analyzed the roles that these genes play in determining the phenotype of maize. We hope that the candidate genes identified here will provide a biological perspective and contribute to maize breeding research.
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19
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Zhang F, Song H, Zeng M, Li Y, Kurgan L, Li M. DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions. Proteomics 2019; 19:e1900019. [PMID: 30941889 DOI: 10.1002/pmic.201900019] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 03/18/2019] [Indexed: 01/06/2023]
Abstract
Annotation of protein functions plays an important role in understanding life at 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 Fmax = 0.54 and AUC = 0.94 on the CAFA3 dataset.
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Affiliation(s)
- Fuhao Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, P. R. China
| | - Hong Song
- School of Computer Science and Engineering, Central South University, Changsha, 410083, P. R. China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, P. R. China
| | - Yaohang Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, P. R. China.,Department of Computer Science, Old Dominion University, Norfolk, VA, 23529, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, P. R. China
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20
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Lin CH, Konecki DM, Liu M, Wilson SJ, Nassar H, Wilkins AD, Gleich DF, Lichtarge O. Multimodal network diffusion predicts future disease-gene-chemical associations. Bioinformatics 2019; 35:1536-1543. [PMID: 30304494 PMCID: PMC6499233 DOI: 10.1093/bioinformatics/bty858] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 09/14/2018] [Accepted: 10/08/2018] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction. RESULTS We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies. AVAILABILITY AND IMPLEMENTATION Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chih-Hsu Lin
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Daniel M Konecki
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Meng Liu
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Stephen J Wilson
- Department of Biochemistry and Molecular Biology, Houston, TX, USA
| | - Huda Nassar
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Angela D Wilkins
- Departments of Molecular and Human Genetics, and Pharmacology, Houston, TX, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA
| | - David F Gleich
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Olivier Lichtarge
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
- Department of Biochemistry and Molecular Biology, Houston, TX, USA
- Departments of Molecular and Human Genetics, and Pharmacology, Houston, TX, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA
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21
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Zhang F, Peng W, Yang Y, Dai W, Song J. A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein⁻Protein Interactive Networks. Genes (Basel) 2019; 10:genes10010031. [PMID: 30626157 PMCID: PMC6356314 DOI: 10.3390/genes10010031] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 12/24/2018] [Accepted: 01/02/2019] [Indexed: 11/16/2022] Open
Abstract
Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein⁻protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks' similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.
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Affiliation(s)
- Fengyu Zhang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
- Computer Center of Kunming University of Science and Technology, Kunming 650093, China.
| | - Yunfei Yang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
| | - Wei Dai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
| | - Junrong Song
- Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China.
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22
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Zhang J, Zou S, Deng L. Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk. BMC Med Genomics 2018; 11:99. [PMID: 30453964 PMCID: PMC6245587 DOI: 10.1186/s12920-018-0414-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Background With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently. Methods In the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins. Results We compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (Fmax) and coverage. Conclusions BiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement. Electronic supplementary material The online version of this article (10.1186/s12920-018-0414-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jingpu Zhang
- School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, 467000, China.,School of Information Science and Engineering, Central South University, Changsha, 410083, China
| | - Shuai Zou
- School of Information Science and Engineering, Central South University, Changsha, 410083, China
| | - Lei Deng
- School of Software, Central South University, Changsha, 410075, China.
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23
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Lan W, Wang J, Li M, Liu J, Wu FX, Pan Y. Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1774-1782. [PMID: 27392365 DOI: 10.1109/tcbb.2016.2586190] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
MicroRNAs (miRNAs) are a type of non-coding RNAs with about ∼22nt nucleotides. Increasing evidences have shown that miRNAs play critical roles in many human diseases. The identification of human disease-related miRNAs is helpful to explore the underlying pathogenesis of diseases. More and more experimental validated associations between miRNAs and diseases have been reported in the recent studies, which provide useful information for new miRNA-disease association discovery. In this study, we propose a computational framework, KBMF-MDI, to predict the associations between miRNAs and diseases based on their similarities. The sequence and function information of miRNAs are used to measure similarity among miRNAs while the semantic and function information of disease are used to measure similarity among diseases, respectively. In addition, the kernelized Bayesian matrix factorization method is employed to infer potential miRNA-disease associations by integrating these data sources. We applied this method to 6,084 known miRNA-disease associations and utilized 5-fold cross validation to evaluate the performance. The experimental results demonstrate that our method can effectively predict unknown miRNA-disease associations.
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24
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Li M, Li W, Wu FX, Pan Y, Wang J. Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information. J Theor Biol 2018; 447:65-73. [PMID: 29571709 DOI: 10.1016/j.jtbi.2018.03.029] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 03/19/2018] [Accepted: 03/20/2018] [Indexed: 01/07/2023]
Abstract
Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC.
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Affiliation(s)
- Min Li
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Wenkai Li
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA.
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
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25
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Peng W, Lan W, Zhong J, Wang J, Pan Y. A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks. Methods 2017; 124:69-77. [DOI: 10.1016/j.ymeth.2017.05.024] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 05/02/2017] [Accepted: 05/28/2017] [Indexed: 01/08/2023] Open
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Abstract
Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery - network propagation - suggesting that it is a powerful data transformation method of broad utility in genetic research.
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Peng W, Lan W, Yu Z, Wang J, Pan Y. A Framework for Integrating Multiple Biological Networks to Predict MicroRNA-Disease Associations. IEEE Trans Nanobioscience 2017; 16:100-107. [DOI: 10.1109/tnb.2016.2633276] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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28
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Abstract
Background Protein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks. Results There exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN. Conclusions The experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance.
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29
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Zhao B, Wang J, Li M, Li X, Li Y, Wu FX, Pan Y. A New Method for Predicting Protein Functions From Dynamic Weighted Interactome Networks. IEEE Trans Nanobioscience 2016; 15:131-9. [PMID: 26955047 DOI: 10.1109/tnb.2016.2536161] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of proteins can only be annotated computationally. Under new conditions or stimuli, not only the number and location of proteins would be changed, but also their interactions. This dynamic feature of protein interactions, however, was not considered in the existing function prediction algorithms. Taking the dynamic nature of protein interactions into consideration, we construct a dynamic weighted interactome network (DWIN) by integrating protein-protein interaction (PPI) network and time course gene expression data, as well as proteins' domain information and protein complex information. Then, we propose a new prediction approach that predicts protein functions from the constructed dynamic weighted interactome network. For an unknown protein, the proposed method visits dynamic networks at different time points and scores functions derived from all neighbors. Finally, the method selects top N functions from these ranked candidate functions to annotate the testing protein. Experiments on PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions. The evaluation results demonstrated that the proposed method outperforms other competing methods.
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