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Koppad S, B A, Gkoutos GV, Acharjee A. Cloud Computing Enabled Big Multi-Omics Data Analytics. Bioinform Biol Insights 2021; 15:11779322211035921. [PMID: 34376975 PMCID: PMC8323418 DOI: 10.1177/11779322211035921] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/12/2021] [Indexed: 12/27/2022] Open
Abstract
High-throughput experiments enable researchers to explore complex multifactorial
diseases through large-scale analysis of omics data. Challenges for such
high-dimensional data sets include storage, analyses, and sharing. Recent
innovations in computational technologies and approaches, especially in cloud
computing, offer a promising, low-cost, and highly flexible solution in the
bioinformatics domain. Cloud computing is rapidly proving increasingly useful in
molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or
proteomics data sets), and for the integration, analysis, and interpretation of
phenotypic data. We review the adoption of advanced cloud-based and big data
technologies for processing and analyzing omics data and provide insights into
state-of-the-art cloud bioinformatics applications.
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Affiliation(s)
- Saraswati Koppad
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - Annappa B
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences and Centre for Computational Biology, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham, Birmingham, UK.,MRC Health Data Research UK (HDR UK), London, UK.,NIHR Experimental Cancer Medicine Centre, Birmingham, UK.,NIHR Biomedical Research Centre, University Hospitals Birmingham, Birmingham, UK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences and Centre for Computational Biology, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.,Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham, Birmingham, UK
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COVID-19 Reducing the Risks: Telemedicine is the New Norm for Surgical Consultations and Communications. Aesthetic Plast Surg 2021; 45:343-348. [PMID: 32885319 PMCID: PMC7471549 DOI: 10.1007/s00266-020-01907-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 07/27/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION COVID-19, a worldwide pandemic, has enforced a national lockdown in the UK which produced a paradigm shift about the way medical practitioners would perform consultations and communication with their patients. Senior authors realised that in lockdown there was only one option to see a patient: virtual consultation via telecommunication technologies. This paper will discuss the current benefits and considerations of Telemedicine, particularly in plastic surgery, to decipher the next route of action to further validate its use for future implementation. METHOD A detailed literature review was carried out comparing papers from 1992 to 2020. A survey of 122 consultant plastic surgeons found an encouraging result as 70% positively embraced the suggestion of Telemedicine in their current practice. DISCUSSION Telemedicine produced equal or improved patient satisfaction. Its utilisation reduced cost for patient, clinic and consultant. With accessibility to a large percentage of the population, Telemedicine enables infection control and adherence to social distancing during COVID-19. Considerations include dependability on internet access, legal aspects, cyber security and General Data Protection Regulation (GDPR), the inability to perform palpation or physical inspection and psychological impacts on the patient. CONCLUSION In modern times, Telemedicine has become more accessible and COVID-19 has made it more applicable than ever before. More in-depth research is needed for validation of this technique within plastic surgery. While maintaining quality of care and a vital role in social distancing, there is a strong need for standardisation of Telemedicine processes, platforms, encryption and data storage. LEVEL OF EVIDENCE V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Alnasir JJ, Shanahan HP. The application of Hadoop in structural bioinformatics. Brief Bioinform 2018; 21:96-105. [PMID: 30462158 DOI: 10.1093/bib/bby106] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/20/2018] [Accepted: 10/05/2018] [Indexed: 11/13/2022] Open
Abstract
The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.
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Affiliation(s)
- Jamie J Alnasir
- Institute of Cancer Research, Old Brompton Road, London, United Kingdom
| | - Hugh P Shanahan
- Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
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Zhang P, Hung LH, Lloyd W, Yeung KY. Hot-starting software containers for STAR aligner. Gigascience 2018; 7:5062793. [PMID: 30085034 PMCID: PMC6131214 DOI: 10.1093/gigascience/giy092] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 04/07/2018] [Accepted: 07/17/2018] [Indexed: 01/22/2023] Open
Abstract
Background Using software containers has become standard practice to reproducibly deploy and execute biomedical workflows on the cloud. However, some applications that contain time-consuming initialization steps will produce unnecessary costs for repeated executions. Findings We demonstrate that hot-starting from containers that have been frozen after the application has already begun execution can speed up bioinformatics workflows by avoiding repetitive initialization steps. We use an open-source tool called Checkpoint and Restore in Userspace (CRIU) to save the state of the containers as a collection of checkpoint files on disk after it has read in the indices. The resulting checkpoint files are migrated to the host, and CRIU is used to regenerate the containers in that ready-to-run hot-start state. As a proof-of-concept example, we create a hot-start container for the spliced transcripts alignment to a reference (STAR) aligner and deploy this container to align RNA sequencing data. We compare the performance of the alignment step with and without checkpoints on cloud platforms using local and network disks. Conclusions We demonstrate that hot-starting Docker containers from snapshots taken after repetitive initialization steps are completed significantly speeds up the execution of the STAR aligner on all experimental platforms, including Amazon Web Services, Microsoft Azure, and local virtual machines. Our method can be potentially employed in other bioinformatics applications in which a checkpoint can be inserted after a repetitive initialization phase.
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Affiliation(s)
- Pai Zhang
- School of Engineering and Technology, Campus Box 358426, 1900 Commerce Street, University of Washington, Tacoma, Washington 98402-3100, USA
| | - Ling-Hong Hung
- School of Engineering and Technology, Campus Box 358426, 1900 Commerce Street, University of Washington, Tacoma, Washington 98402-3100, USA
| | - Wes Lloyd
- School of Engineering and Technology, Campus Box 358426, 1900 Commerce Street, University of Washington, Tacoma, Washington 98402-3100, USA
| | - Ka Yee Yeung
- School of Engineering and Technology, Campus Box 358426, 1900 Commerce Street, University of Washington, Tacoma, Washington 98402-3100, USA
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Zhang H, Chen S, Liu J, Zhou Z, Wu T. An incremental anomaly detection model for virtual machines. PLoS One 2017; 12:e0187488. [PMID: 29117245 PMCID: PMC5678885 DOI: 10.1371/journal.pone.0187488] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 08/30/2017] [Indexed: 11/18/2022] Open
Abstract
Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.
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Affiliation(s)
- Hancui Zhang
- College of Software Engineering, Chongqing University, Chongqing, China
- * E-mail:
| | - Shuyu Chen
- College of Software Engineering, Chongqing University, Chongqing, China
| | - Jun Liu
- College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhen Zhou
- School of computer science and technology, Southwest Minzu University, Chengdu, China
| | - Tianshu Wu
- College of Computer Science, Chongqing University, Chongqing, China
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Jones S, Baizan-Edge A, MacFarlane S, Torrance L. Viral Diagnostics in Plants Using Next Generation Sequencing: Computational Analysis in Practice. FRONTIERS IN PLANT SCIENCE 2017; 8:1770. [PMID: 29123534 PMCID: PMC5662881 DOI: 10.3389/fpls.2017.01770] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 09/28/2017] [Indexed: 05/04/2023]
Abstract
Viruses cause significant yield and quality losses in a wide variety of cultivated crops. Hence, the detection and identification of viruses is a crucial facet of successful crop production and of great significance in terms of world food security. Whilst the adoption of molecular techniques such as RT-PCR has increased the speed and accuracy of viral diagnostics, such techniques only allow the detection of known viruses, i.e., each test is specific to one or a small number of related viruses. Therefore, unknown viruses can be missed and testing can be slow and expensive if molecular tests are unavailable. Methods for simultaneous detection of multiple viruses have been developed, and (NGS) is now a principal focus of this area, as it enables unbiased and hypothesis-free testing of plant samples. The development of NGS protocols capable of detecting multiple known and emergent viruses present in infected material is proving to be a major advance for crops, nuclear stocks or imported plants and germplasm, in which disease symptoms are absent, unspecific or only triggered by multiple viruses. Researchers want to answer the question "how many different viruses are present in this crop plant?" without knowing what they are looking for: RNA-sequencing (RNA-seq) of plant material allows this question to be addressed. As well as needing efficient nucleic acid extraction and enrichment protocols, virus detection using RNA-seq requires fast and robust bioinformatics methods to enable host sequence removal and virus classification. In this review recent studies that use RNA-seq for virus detection in a variety of crop plants are discussed with specific emphasis on the computational methods implemented. The main features of a number of specific bioinformatics workflows developed for virus detection from NGS data are also outlined and possible reasons why these have not yet been widely adopted are discussed. The review concludes by discussing the future directions of this field, including the use of bioinformatics tools for virus detection deployed in analytical environments using cloud computing.
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Affiliation(s)
- Susan Jones
- Information and Computational Science Group, The James Hutton Institute, Dundee, United Kingdom
| | - Amanda Baizan-Edge
- School of Biology, The University of St Andrews, St Andrews, United Kingdom
| | - Stuart MacFarlane
- Cell and Molecular Science Group, The James Hutton Institute, Dundee, United Kingdom
| | - Lesley Torrance
- School of Biology, The University of St Andrews, St Andrews, United Kingdom
- Cell and Molecular Science Group, The James Hutton Institute, Dundee, United Kingdom
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Das AK, Koppa PK, Goswami S, Platania R, Park SJ. Large-scale parallel genome assembler over cloud computing environment. J Bioinform Comput Biol 2017; 15:1740003. [DOI: 10.1142/s0219720017400030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.
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Affiliation(s)
- Arghya Kusum Das
- School of Electrical Engineering and Computer Science, Center for Computation and Technology, Louisiana State University, 340 East Parker Blvd, Baton Rouge, Louisiana 70803, USA
| | - Praveen Kumar Koppa
- School of Electrical Engineering and Computer Science, Center for Computation and Technology, Louisiana State University, 340 East Parker Blvd, Baton Rouge, Louisiana 70803, USA
| | - Sayan Goswami
- School of Electrical Engineering and Computer Science, Center for Computation and Technology, Louisiana State University, 340 East Parker Blvd, Baton Rouge, Louisiana 70803, USA
| | - Richard Platania
- School of Electrical Engineering and Computer Science, Center for Computation and Technology, Louisiana State University, 340 East Parker Blvd, Baton Rouge, Louisiana 70803, USA
| | - Seung-Jong Park
- School of Electrical Engineering and Computer Science, Center for Computation and Technology, Louisiana State University, 340 East Parker Blvd, Baton Rouge, Louisiana 70803, USA
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Bianchi F. Bioinformatics for Clinical Use in Breast Cancer. Breast Cancer 2017. [DOI: 10.1007/978-3-319-48848-6_82] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Abstract
The number of large-scale genomics projects is increasing due to the availability of affordable high-throughput sequencing (HTS) technologies. The use of HTS for bacterial infectious disease research is attractive because one whole-genome sequencing (WGS) run can replace multiple assays for bacterial typing, molecular epidemiology investigations, and more in-depth pathogenomic studies. The computational resources and bioinformatics expertise required to accommodate and analyze the large amounts of data pose new challenges for researchers embarking on genomics projects for the first time. Here, we present a comprehensive overview of a bacterial genomics projects from beginning to end, with a particular focus on the planning and computational requirements for HTS data, and provide a general understanding of the analytical concepts to develop a workflow that will meet the objectives and goals of HTS projects.
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Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populations. BMC Bioinformatics 2016; 17:292. [PMID: 27465705 PMCID: PMC4963998 DOI: 10.1186/s12859-016-1159-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 07/21/2016] [Indexed: 11/24/2022] Open
Abstract
Background Next generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut microflora, combined with the sustained exponential growth in sequencing capacity, has led to the challenge of identifying microbial organisms by DNA sequence. We sought to validate a Scalable Metagenomics Alignment Research Tool (SMART), a novel searching heuristic for shotgun metagenomics sequencing results. Results After retrieving all genomic DNA sequences from the NCBI GenBank, over 1 × 1011 base pairs of 3.3 × 106 sequences from 9.25 × 105 species were indexed using 4 base pair hashtable shards. A MapReduce searching strategy was used to distribute the search workload in a computing cluster environment. In addition, a one base pair permutation algorithm was used to account for single nucleotide polymorphisms and sequencing errors. Simulated datasets used to evaluate Kraken, a similar metagenomics classification tool, were used to measure and compare precision and accuracy. Finally using a same set of training sequences we compared Kraken, CLARK, and SMART within the same computing environment. Utilizing 12 computational nodes, we completed the classification of all datasets in under 10 min each using exact matching with an average throughput of over 1.95 × 106 reads classified per minute. With permutation matching, we achieved sensitivity greater than 83 % and precision greater than 94 % with simulated datasets at the species classification level. We demonstrated the application of this technique applied to conjunctival and gut microbiome metagenomics sequencing results. In our head to head comparison, SMART and CLARK had similar accuracy gains over Kraken at the species classification level, but SMART required approximately half the amount of RAM of CLARK. Conclusions SMART is the first scalable, efficient, and rapid metagenomics classification algorithm capable of matching against all the species and sequences present in the NCBI GenBank and allows for a single step classification of microorganisms as well as large plant, mammalian, or invertebrate genomes from which the metagenomic sample may have been derived.
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Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm. PLoS One 2016; 11:e0158102. [PMID: 27384239 PMCID: PMC4934704 DOI: 10.1371/journal.pone.0158102] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Accepted: 06/12/2016] [Indexed: 11/19/2022] Open
Abstract
Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.
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Lelieveld SH, Veltman JA, Gilissen C. Novel bioinformatic developments for exome sequencing. Hum Genet 2016; 135:603-14. [PMID: 27075447 PMCID: PMC4883269 DOI: 10.1007/s00439-016-1658-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 03/15/2016] [Indexed: 01/19/2023]
Abstract
With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.
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Affiliation(s)
- Stefan H Lelieveld
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Joris A Veltman
- Department of Human Genetics, Donders Centre for Neuroscience, Radboudumc, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
- Department of Clinical Genetics, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
| | - Christian Gilissen
- Department of Human Genetics, Donders Centre for Neuroscience, Radboudumc, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
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Lelieveld SH, Veltman JA, Gilissen C. Novel bioinformatic developments for exome sequencing. Hum Genet 2016. [PMID: 27075447 DOI: 10.1007/s00439‐016‐1658‐6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.
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Affiliation(s)
- Stefan H Lelieveld
- Department of Human Genetics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Joris A Veltman
- Department of Human Genetics, Donders Centre for Neuroscience, Radboudumc, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.,Department of Clinical Genetics, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Universiteitssingel 50, 6229 ER, Maastricht, The Netherlands
| | - Christian Gilissen
- Department of Human Genetics, Donders Centre for Neuroscience, Radboudumc, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
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Sandhu R, Gill HK, Sood SK. Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing. JOURNAL OF COMPUTATIONAL SCIENCE 2016; 12:11-22. [PMID: 32362959 PMCID: PMC7185782 DOI: 10.1016/j.jocs.2015.11.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2015] [Revised: 10/30/2015] [Accepted: 11/04/2015] [Indexed: 05/07/2023]
Abstract
H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.
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Affiliation(s)
- Rajinder Sandhu
- Department of Computer Science and Engineering, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab, India
| | - Harsuminder K. Gill
- Department of Computer Science and Engineering, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab, India
| | - Sandeep K. Sood
- Department of Computer Science and Engineering, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab, India
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Kao HY, Wu WH, Liang TY, Lee KT, Hou MF, Shi HY. Cloud-Based Service Information System for Evaluating Quality of Life after Breast Cancer Surgery. PLoS One 2015; 10:e0139252. [PMID: 26422018 PMCID: PMC4589455 DOI: 10.1371/journal.pone.0139252] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Accepted: 09/09/2015] [Indexed: 11/18/2022] Open
Abstract
Objective Although recent studies have improved understanding of quality of life (QOL) outcomes of breast conserving surgery, few have used longitudinal data for more than two time points, and few have examined predictors of QOL over two years. Additionally, the longitudinal data analyses in such studies rarely apply the appropriate statistical methodology to control for censoring and inter-correlations arising from repeated measures obtained from the same patient pool. This study evaluated an internet-based system for measuring longitudinal changes in QOL and developed a cloud-based system for managing patients after breast conserving surgery. Methods This prospective study analyzed 657 breast cancer patients treated at three tertiary academic hospitals. Related hospital personnel such as surgeons and other healthcare professionals were also interviewed to determine the requirements for an effective cloud-based system for surveying QOL in breast cancer patients. All patients completed the SF-36, Quality of Life Questionnaire (QLQ-C30) and its supplementary breast cancer measure (QLQ-BR23) at baseline, 6 months, 1 year, and 2 years postoperatively. The 95% confidence intervals for differences in responsiveness estimates were derived by bootstrap estimation. Scores derived by these instruments were interpreted by generalized estimating equation before and after surgery. Results All breast cancer surgery patients had significantly improved QLQ-C30 and QLQ-BR23 subscale scores throughout the 2-year follow-up period (p<0.05). During the study period, QOL generally had a negative association with advanced age, high Charlson comorbidity index score, tumor stage III or IV, previous chemotherapy, and long post-operative LOS. Conversely, QOL was positively associated with previous radiotherapy and hormone therapy. Additionally, patients with high scores for preoperative QOL tended to have high scores for QLQ-C30, QLQ-BR23 and SF-36 subscales. Based on the results of usability testing, the five constructs were rated on a Likert scale from 1–7 as follows: system usefulness (5.6±1.8), ease of use (5.6±1.5), information quality (5.4±1.4), interface quality (5.5±1.4), and overall satisfaction (5.5±1.6). Conclusions The current trend in clinical medicine is applying therapies and interventions that improve QOL. Therefore, a potentially vast amount of internet-based QOL data is available for use in defining patient populations that may benefit from therapeutic intervention. Additionally, before undergoing breast conserving surgery, patients should be advised that their postoperative QOL depends not only on the success of the surgery, but also on their preoperative functional status.
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Affiliation(s)
- Hao-Yun Kao
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan, R.O.C.
| | - Wen-Hsiung Wu
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan, R.O.C.
| | - Tyng-Yeu Liang
- Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, R.O.C.
| | - King-The Lee
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan, R.O.C.
| | - Ming-Feng Hou
- Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, R.O.C.
- Cancer Center, Kaohsiung Medical University Hospital, 807 Kaohsiung, Taiwan, R.O.C.
- National Sun Yat-Sen University-Kaohsiung Medical University Joint Research Center, 80708 Kaohsiung, Taiwan, R.O.C.
| | - Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan, R.O.C.
- * E-mail:
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