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Du J, Lin D, Yuan R, Chen X, Liu X, Yan J. Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus. Front Genet 2021; 12:779186. [PMID: 34899863 PMCID: PMC8657768 DOI: 10.3389/fgene.2021.779186] [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: 09/18/2021] [Accepted: 10/20/2021] [Indexed: 11/25/2022] Open
Abstract
Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.
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Affiliation(s)
| | | | | | | | | | - Jing Yan
- Zhejiang Hospital, Hangzhou, China.,Zhejiang Provincial Key Lab of Geriatrics, Zhejiang Hospital, Hangzhou, China
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2
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Peng J, Guan J, Hui W, Shang X. A novel subnetwork representation learning method for uncovering disease-disease relationships. Methods 2020; 192:77-84. [PMID: 32946974 DOI: 10.1016/j.ymeth.2020.09.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/20/2020] [Accepted: 09/07/2020] [Indexed: 12/12/2022] Open
Abstract
Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps://github.com/MedicineBiology-AI/SubNet2vec.git.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Jiaojiao Guan
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Weiwei Hui
- Vivo mobile communications (Hang Zhou) co. LTD, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
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3
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Peng J, Xue H, Hui W, Lu J, Chen B, Jiang Q, Shang X, Wang Y. An online tool for measuring and visualizing phenotype similarities using HPO. BMC Genomics 2018; 19:571. [PMID: 30367579 PMCID: PMC6101067 DOI: 10.1186/s12864-018-4927-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background The Human Phenotype Ontology (HPO) is one of the most popular bioinformatics resources. Recently, HPO-based phenotype semantic similarity has been effectively applied to model patient phenotype data. However, the existing tools are revised based on the Gene Ontology (GO)-based term similarity. The design of the models are not optimized for the unique features of HPO. In addition, existing tools only allow HPO terms as input and only provide pure text-based outputs. Results We present PhenoSimWeb, a web application that allows researchers to measure HPO-based phenotype semantic similarities using four approaches borrowed from GO-based similarity measurements. Besides, we provide a approach considering the unique properties of HPO. And, PhenoSimWeb allows text that describes phenotypes as input, since clinical phenotype data is always in text. PhenoSimWeb also provides a graphic visualization interface to visualize the resulting phenotype network. Conclusions PhenoSimWeb is an easy-to-use and functional online application. Researchers can use it to calculate phenotype similarity conveniently, predict phenotype associated genes or diseases, and visualize the network of phenotype interactions. PhenoSimWeb is available at http://120.77.47.2:8080.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Hansheng Xue
- Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Junya Lu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Bolin Chen
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Yadong Wang
- Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China. .,School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
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4
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Abstract
BACKGROUND Recently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity. RESULTS We proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity. CONCLUSIONS PhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
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5
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Wang Z, Wu X, Wang Y. A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip. BMC Bioinformatics 2018; 19:115. [PMID: 29671397 PMCID: PMC5907140 DOI: 10.1186/s12859-018-2096-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background DNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases. Results Here we proposed a generalized framework for evaluating data analysis methods for Illumina 450K array. This framework considers the following steps towards a successful analysis: importing data, quality control, within-array normalization, correcting type bias, detecting differentially methylated probes or regions and biological interpretation. Conclusions We evaluated five methods using three real datasets, and proposed outperform methods for the Illumina 450K array data analysis. Minfi and methylumi are optimal choice when analyzing small dataset. BMIQ and RCP are proper to correcting type bias and the normalized result of them can be used to discover DMPs. R package missMethyl is suitable for GO term enrichment analysis and biological interpretation.
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Affiliation(s)
- Zhenxing Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - XiaoLiang Wu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
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Chu Y, Teng M, Wang Y. Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring. BMC Bioinformatics 2018; 19:112. [PMID: 29671389 PMCID: PMC5907144 DOI: 10.1186/s12859-018-2099-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias. RESULTS We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal's superiority to exsiting GC-bias correction methods at any level of subclonal population. CONCLUSIONS Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.
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Affiliation(s)
- Yanshuo Chu
- Center for Bioinformatics, Harbin Institute of Technology, Harbin, China
| | - Mingxiang Teng
- Center for Bioinformatics, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- Center for Bioinformatics, Harbin Institute of Technology, Harbin, China.
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Hao X, Hao J, Wang L, Hou H. Effective norm emergence in cell systems under limited communication. BMC Bioinformatics 2018; 19:119. [PMID: 29671391 PMCID: PMC5907317 DOI: 10.1186/s12859-018-2097-2] [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] [Indexed: 11/24/2022] Open
Abstract
Background The cooperation of cells in biological systems is similar to that of agents in cooperative multi-agent systems. Research findings in multi-agent systems literature can provide valuable inspirations to biological research. The well-coordinated states in cell systems can be viewed as desirable social norms in cooperative multi-agent systems. One important research question is how a norm can rapidly emerge with limited communication resources. Results In this work, we propose a learning approach which can trade off the agents’ performance of coordinating on a consistent norm and the communication cost involved. During the learning process, the agents can dynamically adjust their coordination set according to their own observations and pick out the most crucial agents to coordinate with. In this way, our method significantly reduces the coordination dependence among agents. Conclusion The experiment results show that our method can efficiently facilitate the social norm emergence among agents, and also scale well to large-scale populations.
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Affiliation(s)
- Xiaotian Hao
- School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China
| | - Jianye Hao
- School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China
| | - Li Wang
- School of Computer Science and Software, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China.
| | - Hanxu Hou
- School of Electrical Engineering and Intelligentization, Dongguan University of Technology, No. 1, university road, songshan lake district, dongguan, 221116, China.
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Sun S, Sun X, Zheng Y. Higher-order partial least squares for predicting gene expression levels from chromatin states. BMC Bioinformatics 2018; 19:113. [PMID: 29671394 PMCID: PMC5907142 DOI: 10.1186/s12859-018-2100-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Background Extensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might borrow more unknown interactions in chromatin states to predicting gene expression levels. Results The prediction models were learned from regions around upstream 10k base pairs and downstream 10k base pairs of the transcriptional start sites (TSSs) on three species (i.e., Human, Rhesus Macaque, and Chimpanzee) with five histone modifications (i.e., H3K4me1, H3K4me3, H3K27ac, H3K27me3, and Pol II). Experimental results demonstrate that the proposed method is more powerful to predicting gene expression levels than several other popular methods. Specifically, our method enable to get more powerful performance on both commonly used criteria, R and RMSE, as high as 1.7% and 11%, respectively. Conclusions The overall aim of this work is to show that the higher-order representation is able to include more unknown interaction information between histone modifications across different species.
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Affiliation(s)
- Shiquan Sun
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, People's Republic of China. .,Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA.
| | - Xifang Sun
- School of Science, Xi'an Shiyou University, Xi'an, 710065, Shaanxi, People's Republic of China
| | - Yan Zheng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, People's Republic of China
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Tou H, Yao L, Wei Z, Zhuang X, Zhang B. Automatic infection detection based on electronic medical records. BMC Bioinformatics 2018; 19:117. [PMID: 29671399 PMCID: PMC5907141 DOI: 10.1186/s12859-018-2101-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Making accurate patient care decision, as early as possible, is a constant challenge, especially for physicians in the emergency department. The increasing volumes of electronic medical records (EMRs) open new horizons for automatic diagnosis. In this paper, we propose to use machine learning approaches for automatic infection detection based on EMRs. Five categories of information are utilized for prediction, including personal information, admission note, vital signs, diagnose test results and medical image diagnose. RESULTS Experimental results on a newly constructed EMRs dataset from emergency department show that machine learning models can achieve a decent performance for infection detection with area under the receiver operator characteristic curve (AUC) of 0.88. Out of all the five types of information, admission note in text form makes the most contribution with the AUC of 0.87. CONCLUSIONS This study provides a state-of-the-art EMRs processing system to automatically make medical decisions. It extracts five types of features associated with infection and achieves a decent performance on automatic infection detection based on machine learning models.
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Affiliation(s)
- Huaixiao Tou
- School of Data Science, Fudan University, Shanghai, China
| | - Lu Yao
- Zhongshan Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhongyu Wei
- School of Data Science, Fudan University, Shanghai, China.
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China
| | - Bo Zhang
- Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.
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Guo Y, Liu S, Li Z, Shang X. BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data. BMC Bioinformatics 2018; 19:118. [PMID: 29671390 PMCID: PMC5907304 DOI: 10.1186/s12859-018-2095-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. RESULTS In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. CONCLUSIONS The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes by using deep learning on high-dimensional and small-scale biology data.
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Affiliation(s)
- Yang Guo
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072 People’s Republic of China
| | - Shuhui Liu
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072 People’s Republic of China
| | - Zhanhuai Li
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072 People’s Republic of China
| | - Xuequn Shang
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072 People’s Republic of China
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Peng J, Zhang X, Hui W, Lu J, Li Q, Liu S, Shang X. Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach. BMC SYSTEMS BIOLOGY 2018; 12:18. [PMID: 29560823 PMCID: PMC5861498 DOI: 10.1186/s12918-018-0539-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. RESULTS We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. CONCLUSIONS Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China. .,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, China. .,Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Xuanshuo Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junya Lu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Qianqian Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Shuhui Liu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.,Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, China
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Peng J, Wang H, Lu J, Hui W, Wang Y, Shang X. Identifying term relations cross different gene ontology categories. BMC Bioinformatics 2017; 18:573. [PMID: 29297309 PMCID: PMC5751813 DOI: 10.1186/s12859-017-1959-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecular function, biological process and cellular component. For better biological reasoning, identifying the biological relationships between terms in different categories are important. However, the existing measurements to calculate similarity between terms in different categories are either developed by using the GO data only or only take part of combined gene co-function network information. Results We propose an iterative ranking-based method called CroGO2 to measure the cross-categories GO term similarities by incorporating level information of GO terms with both direct and indirect interactions in the gene co-function network. Conclusions The evaluation test shows that CroGO2 performs better than the existing methods. A genome-specific term association network for yeast is also generated by connecting terms with the high confidence score. The linkages in the term association network could be supported by the literature. Given a gene set, the related terms identified by using the association network have overlap with the related terms identified by GO enrichment analysis.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Honggang Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Junya Lu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
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Affiliation(s)
- Sriganesh Srihari
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland 4072, Australia; Current address: South Australian Health and Medical Research Institute, Adelaide 5001, South Australia, Australia.
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