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Cirinciani M, Da Pozzo E, Trincavelli ML, Milazzo P, Martini C. Drug Mechanism: A bioinformatic update. Biochem Pharmacol 2024; 228:116078. [PMID: 38402909 DOI: 10.1016/j.bcp.2024.116078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/01/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.
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Affiliation(s)
- Martina Cirinciani
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy
| | - Eleonora Da Pozzo
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Maria Letizia Trincavelli
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Paolo Milazzo
- Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy; Department of Computer Science, University of Pisa, Largo Pontecorvo, 3, 56127 Pisa, Italy
| | - Claudia Martini
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy.
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Park S, Silva E, Singhal A, Kelly MR, Licon K, Panagiotou I, Fogg C, Fong S, Lee JJY, Zhao X, Bachelder R, Parker BA, Yeung KT, Ideker T. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors. NATURE CANCER 2024; 5:996-1009. [PMID: 38443662 PMCID: PMC11286358 DOI: 10.1038/s43018-024-00740-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 02/07/2024] [Indexed: 03/07/2024]
Abstract
Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor's genetic profile modulates CDK4/6i resistance.
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Affiliation(s)
- Sungjoon Park
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Erica Silva
- Program in Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Akshat Singhal
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Marcus R Kelly
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Kate Licon
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Isabella Panagiotou
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Catalina Fogg
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Samson Fong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - John J Y Lee
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xiaoyu Zhao
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Robin Bachelder
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Barbara A Parker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Kay T Yeung
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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Zhao X, Singhal A, Park S, Kong J, Bachelder R, Ideker T. Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress. Cancer Discov 2024; 14:508-523. [PMID: 38236062 PMCID: PMC10905674 DOI: 10.1158/2159-8290.cd-23-0641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/25/2023] [Accepted: 12/21/2023] [Indexed: 01/19/2024]
Abstract
Rapid proliferation is a hallmark of cancer associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multidrug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK-JAK-STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine. SIGNIFICANCE Zhao and colleagues use recent advances in machine learning to study the effects of tumor mutations on the response to common therapeutics that cause RS. The resulting predictive models integrate numerous genetic alterations distributed across a constellation of molecular assemblies, facilitating a quantitative and interpretable assessment of drug response. This article is featured in Selected Articles from This Issue, p. 384.
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Affiliation(s)
- Xiaoyu Zhao
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - Akshat Singhal
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California
| | - Sungjoon Park
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - JungHo Kong
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
- Moores Cancer Center, School of Medicine, University of California, San Diego, La Jolla, California
| | - Robin Bachelder
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
| | - Trey Ideker
- Division of Human Genomics and Precision Medicine, Department of Medicine, University of California, San Diego, La Jolla, California
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California
- Moores Cancer Center, School of Medicine, University of California, San Diego, La Jolla, California
- Department of Bioengineering, University of California, San Diego, La Jolla, California
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Esser-Skala W, Fortelny N. Reliable interpretability of biology-inspired deep neural networks. NPJ Syst Biol Appl 2023; 9:50. [PMID: 37816807 PMCID: PMC10564878 DOI: 10.1038/s41540-023-00310-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 09/15/2023] [Indexed: 10/12/2023] Open
Abstract
Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where real-world concepts are encoded in hidden nodes, which can be ranked by importance and thereby interpreted. In such models trained on single-cell transcriptomes, we previously demonstrated that node-level interpretations lack robustness upon repeated training and are influenced by biases in biological knowledge. Similar studies are missing for related models. Here, we test and extend our methodology for reliable interpretability in P-NET, a biology-inspired model trained on patient mutation data. We observe variability of interpretations and susceptibility to knowledge biases, and identify the network properties that drive interpretation biases. We further present an approach to control the robustness and biases of interpretations, which leads to more specific interpretations. In summary, our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning.
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Affiliation(s)
- Wolfgang Esser-Skala
- Computational Systems Biology Group, Department of Biosciences and Medical Biology, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
| | - Nikolaus Fortelny
- Computational Systems Biology Group, Department of Biosciences and Medical Biology, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.
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Liu Y, Zhang YZ, Imoto S. Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases. PLoS One 2023; 18:e0290307. [PMID: 37603579 PMCID: PMC10441785 DOI: 10.1371/journal.pone.0290307] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023] Open
Abstract
The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates' important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. The discovered candidates' important microbiome genes and their functions provide novel insights into microbe functional contribution.
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Affiliation(s)
- Yunjie Liu
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yao-zhong Zhang
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Seiya Imoto
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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Sinha K, Ghosh N, Sil PC. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies. Chem Res Toxicol 2023; 36:1174-1205. [PMID: 37561655 DOI: 10.1021/acs.chemrestox.2c00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.
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Affiliation(s)
- Krishnendu Sinha
- Department of Zoology, Jhargram Raj College, Jhargram 721507, West Bengal, India
| | - Nabanita Ghosh
- Department of Zoology, Maulana Azad College, Kolkata 700013, West Bengal, India
| | - Parames C Sil
- Division of Molecular Medicine, Bose Institute, Kolkata 700054, West Bengal, India
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Wang Z, Zhou Y, Zhang Y, Mo YK, Wang Y. XMR: an explainable multimodal neural network for drug response prediction. FRONTIERS IN BIOINFORMATICS 2023; 3:1164482. [PMID: 37600972 PMCID: PMC10433751 DOI: 10.3389/fbinf.2023.1164482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 07/14/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction: Existing large-scale preclinical cancer drug response databases provide us with a great opportunity to identify and predict potentially effective drugs to combat cancers. Deep learning models built on these databases have been developed and applied to tackle the cancer drug-response prediction task. Their prediction has been demonstrated to significantly outperform traditional machine learning methods. However, due to the "black box" characteristic, biologically faithful explanations are hardly derived from these deep learning models. Interpretable deep learning models that rely on visible neural networks (VNNs) have been proposed to provide biological justification for the predicted outcomes. However, their performance does not meet the expectation to be applied in clinical practice. Methods: In this paper, we develop an XMR model, an eXplainable Multimodal neural network for drug Response prediction. XMR is a new compact multimodal neural network consisting of two sub-networks: a visible neural network for learning genomic features and a graph neural network (GNN) for learning drugs' structural features. Both sub-networks are integrated into a multimodal fusion layer to model the drug response for the given gene mutations and the drug's molecular structures. Furthermore, a pruning approach is applied to provide better interpretations of the XMR model. We use five pathway hierarchies (cell cycle, DNA repair, diseases, signal transduction, and metabolism), which are obtained from the Reactome Pathway Database, as the architecture of VNN for our XMR model to predict drug responses of triple negative breast cancer. Results: We find that our model outperforms other state-of-the-art interpretable deep learning models in terms of predictive performance. In addition, our model can provide biological insights into explaining drug responses for triple-negative breast cancer. Discussion: Overall, combining both VNN and GNN in a multimodal fusion layer, XMR captures key genomic and molecular features and offers reasonable interpretability in biology, thereby better predicting drug responses in cancer patients. Our model would also benefit personalized cancer therapy in the future.
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Affiliation(s)
- Zihao Wang
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN, United States
| | - Yun Zhou
- Department of Environmental and Occupational Health, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Yu Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States
| | - Yu K. Mo
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN, United States
| | - Yijie Wang
- Department of Computer Science, Indiana University Bloomington, Bloomington, IN, United States
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Samal BR, Loers JU, Vermeirssen V, De Preter K. Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells. FRONTIERS IN BIOINFORMATICS 2022; 2:1036963. [PMID: 36466148 PMCID: PMC9714662 DOI: 10.3389/fbinf.2022.1036963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/03/2022] [Indexed: 01/02/2024] Open
Abstract
In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.
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Affiliation(s)
- Bikash Ranjan Samal
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Jens Uwe Loers
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Vanessa Vermeirssen
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Center for Medical Genetics Ghent (CMGG), Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
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Ghosh Roy G, Geard N, Verspoor K, He S. MPVNN: Mutated Pathway Visible Neural Network architecture for interpretable prediction of cancer-specific survival risk. Bioinformatics 2022; 38:5026-5032. [PMID: 36124954 DOI: 10.1093/bioinformatics/btac636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/04/2022] [Accepted: 09/16/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with a lack of interpretability. More interpretable visible neural network architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. RESULTS We propose a novel Mutated Pathway Visible Neural Network (MPVNN) architecture, designed using prior signaling pathway knowledge and random replacement of known pathway edges using gene mutation data simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction of MPVNN over other similar-sized NN and standard survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that is important in risk prediction for particular cancer types, is reliable. AVAILABILITY AND IMPLEMENTATION The data and code are available at https://github.com/gourabghoshroy/MPVNN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gourab Ghosh Roy
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.,School of Computing and Information Systems, University of Melbourne, Melbourne 3052, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, University of Melbourne, Melbourne 3052, Australia
| | - Karin Verspoor
- School of Computing and Information Systems, University of Melbourne, Melbourne 3052, Australia.,School of Computing Technologies, RMIT University, Melbourne 3000, Australia
| | - Shan He
- School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
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