The mapping and reducing functions receive not just values, but (key, value) pairs. The authors reported an accuracy of 87% classification, which would not have been as high if they had used just fMRI images or SNP alone. Here we focus on pathway analysis, in which functional effects of genes differentially expressed in an experiment or gene set of particular interest are analyzed, and the reconstruction of networks, where the signals measured using high-throughput techniques are analyzed to reconstruct underlying regulatory networks. Copyright © 2020 Elsevier B.V. or its licensors or contributors. There are multitude of challenges in terms of analyzing genome-scale data including the experiment and inherent biological noise, differences among experimental platforms, and connecting gene expression to reaction flux used in constraint-based methods [170, 171]. Dryad is a distributed execution engine to run big data applications in the form of directed acyclic graph (DAG). If John Doe is an employee of the company, then there will be a relationship between the employee and the department to which he belongs. 1. Research in neurology has shown interest in electrophysiologic monitoring of patients to not only examine complex diseases under a new light but also develop next generation diagnostics and therapeutic devices. One early attempt in this direction is Apache Ambari, although further works still needs under taking, such as integration of the system with cloud infrastructure. However, in addition to the data size issues, physiological signals also pose complexity of a spatiotemporal nature. The data is collected and loaded to a storage environment like Hadoop or NoSQL. Data of different structures needs to be processed. This results from strong coupling among different systems within the body (e.g., interactions between heart rate, respiration, and blood pressure) thereby producing potential markers for clinical assessment. A. Bartell, J. J. Saucerman, and J. The authors of this article do not make specific recommendations about treatment, imaging, and intraoperative monitoring; instead they examine the potentials and implications of neuromonitoring with differeing quality of data and also provide guidance on developing research and application in this area. However, there are a few methods developed for big data compression. However, in the recent past, there has been an increase in the attempts towards utilizing telemetry and continuous physiological time series monitoring to improve patient care and management [77–80]. Currently healthcare systems use numerous disparate and continuous monitoring devices that utilize singular physiological waveform data or discretized vital information to provide alert mechanisms in case of overt events. MapReduce [17] is one of the most popular programming models for big data processing using large-scale commodity clusters. Reconstruction of a gene regulatory network on a genome-scale system as a dynamical model is computationally intensive [135]. The major feature of Spark that makes it unique is its ability to perform in-memory computations. Similarly, there are other proposed techniques for profiling of MapReduce applications to find possible bottlenecks and simulate various scenarios for performance analysis of the modified applications [48]. Apache Pig is a structured query language (SQL)-like environment developed at Yahoo [41] is being used by many organizations like Yahoo, Twitter, AOL, LinkedIn, etc. A scalable infrastructure for developing a patient care management system has been proposed which combines static data and stream data monitored from critically ill patients in the ICU for data mining and alerting medical staff of critical events in real time [113]. Farhad Mehdipour, ... Bahman Javadi, in Advances in Computers, 2016. According to this study simultaneous evaluation of all the available imaging techniques is an unmet need. Besides the huge space required for storing all the data and their analysis, finding the map and dependencies among different data types are challenges for which there is no optimal solution yet. The proposed SP system performs lossless compression through the matching and unification of patterns. However, such uncompounded approaches towards development and implementation of alarm systems tend to be unreliable and their sheer numbers could cause “alarm fatigue” for both care givers and patients [10–12]. Pantelopoulos and Bourbakis discussed the research and development of wearable biosensor systems and identified the advantages and shortcomings in this area of study [125]. For example, MIMIC II [108, 109] and some other datasets included in Physionet [96] provide waveforms and other clinical data from a wide variety of actual patient cohorts. Future research should consider the characteristics of the Big Data system, integrating multicore technologies, multi-GPU models, and new storage devices into Hadoop for further performance enhancement of the system. A computer-aided decision support system was developed by Chen et al. Healthcare is a prime example of how the three Vs of data, velocity (speed of generation of data), variety, and volume [4], are an innate aspect of the data it produces. For instance, a hybrid machine learning method has been developed in [49] that classifies schizophrenia patients and healthy controls using fMRI images and single nucleotide polymorphism (SNP) data [49]. The actual state of each node or set of nodes is determined by using Boolean operations on the state of other nodes in the network [153]. However, static data does not always provide true time context and, hence, when combining the waveform data with static electronic health record data, the temporal nature of the time context during integration can also add significantly to the challenges. In this framework, a cluster of heterogeneous computing nodes with a maximum of 42 concurrent map tasks was set up and the speedup around 100 was achieved. Ashwin Belle, Raghuram Thiagarajan, and S. M. Reza Soroushmehr contributed equally to this work. Another study shows the use of physiological waveform data along with clinical data from the MIMIC II database for finding similarities among patients within the selected cohorts [118]. Review articles are excluded from this waiver policy. For instance, Starfish [47] is a Hadoop-based framework, which aimed to improve the performance of MapReduce jobs using data lifecycle in analytics. This can be overcome over a period of time as the data is processed effectively through the system multiple times, increasing the quality and volume of content available for reference processing. Our work aims at pushing the boundary of computer science in the area of algorithms and systems for large-scale computations. Figure 11.5 shows the different stages involved in the processing of Big Data; the approach to processing Big Data is: While the stages are similar to traditional data processing the key differences are: Data is first analyzed and then processed. For performing analytics on continuous telemetry waveforms, a module like Spark is especially useful since it provides capabilities to ingest and compute on streaming data along with machine learning and graphing tools. Challenges facing medical image analysis. The Spark developers have also proposed an entire data processing stack called Berkeley data analytics stack [50]. A. MacKey, R. D. George et al., “A new microarray, enriched in pancreas and pancreatic cancer cdnas to identify genes relevant to pancreatic cancer,”, G. Bindea, B. Mlecnik, H. Hackl et al., “Cluego: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks,”, G. Bindea, J. Galon, and B. Mlecnik, “CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data,”, A. Subramanian, P. Tamayo, V. K. Mootha et al., “Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles,”, V. K. Mootha, C. M. Lindgren, K.-F. Eriksson et al., “PGC-1, S. Draghici, P. Khatri, A. L. Tarca et al., “A systems biology approach for pathway level analysis,”, M.-H. Teiten, S. Eifes, S. Reuter, A. Duvoix, M. Dicato, and M. Diederich, “Gene expression profiling related to anti-inflammatory properties of curcumin in K562 leukemia cells,”, I. Thiele, N. Swainston, R. M. T. Fleming et al., “A community-driven global reconstruction of human metabolism,”, O. Folger, L. Jerby, C. Frezza, E. Gottlieb, E. Ruppin, and T. Shlomi, “Predicting selective drug targets in cancer through metabolic networks,”, D. Marbach, J. C. Costello, R. Küffner et al., “Wisdom of crowds for robust gene network inference,”, R.-S. Wang, A. Saadatpour, and R. Albert, “Boolean modeling in systems biology: an overview of methodology and applications,”, W. Gong, N. Koyano-Nakagawa, T. Li, and D. J. Garry, “Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data,”, K. C. Chen, L. Calzone, A. Csikasz-Nagy, F. R. Cross, B. Novak, and J. J. Tyson, “Integrative analysis of cell cycle control in budding yeast,”, S. Kimura, K. Ide, A. Kashihara et al., “Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm,”, J. Gebert, N. Radde, and G.-W. Weber, “Modeling gene regulatory networks with piecewise linear differential equations,”, J. N. Bazil, K. D. Stamm, X. Li et al., “The inferred cardiogenic gene regulatory network in the mammalian heart,”, D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano, and G. Stolovitzky, “Revealing strengths and weaknesses of methods for gene network inference,”, N. C. Duarte, S. A. Becker, N. Jamshidi et al., “Global reconstruction of the human metabolic network based on genomic and bibliomic data,”, K. Raman and N. Chandra, “Flux balance analysis of biological systems: applications and challenges,”, C. S. Henry, M. Dejongh, A. Our current trends updated technical team has full of certified engineers and experienced professionals to provide precise guidance for research … Analytics of high-throughput sequencing techniques in genomics is an inherently big data problem as the human genome consists of 30,000 to 35,000 genes [16, 17]. And choose one area i.e. The volume of medical images is growing exponentially. Stephen Bonner, ... Georgios Theodoropoulos, in Software Architecture for Big Data and the Cloud, 2017. A. Seibert, “Modalities and data acquisition,” in, B. J. The integration of medical images with other types of electronic health record (EHR) data and genomic data can also improve the accuracy and reduce the time taken for a diagnosis. These three areas do not comprehensively reflect the application of big data analytics in medicine; instead they are intended to provide a perspective of broad, popular areas of research where the concepts of big data analytics are currently being applied. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. The second generation includes functional class scoring approaches which incorporate expression level changes in individual genes as well as functionally similar genes [25]. This trend reveals that using simple Hadoop setup would not be efficient for big data analytics, and new tools and techniques to automate provisioning decisions should be designed and developed. There have been several indigenous and off-the-shelf efforts in developing and implementing systems that enable such data capture [85, 96–99]. Big data processing is a set of techniques or programming models to access large-scale data to extract useful information for supporting and providing decisions. Boolean networks are extremely useful when amount of quantitative data is small [135, 153] but yield high number of false positives (i.e., when a given condition is satisfied while actually that is not the case) that may be reduced by using prior knowledge [176, 177]. Lastly, some open questions are also proposed and discussed. However, in order to make it clinically applicable for patients, the interaction of radiology, nuclear medicine, and biology is crucial [35] that could complicate its automated analysis. The improvement of the MapReduce programming model is generally confined to a particular aspect, thus the shared memory platform was needed. Employing multimodal data could be beneficial for this purpose [, Reducing the volume of data while maintaining important data such as anatomically relevant data [, Developing scalable/parallel methods and frameworks to speed up the analysis/processing [, Aligning consecutive slices/frames from one scan or corresponding images from different modalities [, Integrity, privacy, and confidentiality of data must be protected [, Delineation of anatomical structure such as vessels and bones [, Finding dependencies/patterns among multimodal data and/or the data captured at different time points in order to increase the accuracy of diagnosis, prediction, and overall performance of the system [, Assessing the performance or accuracy of the system/method. How will users interact and use the metadata? Important physiological and pathophysiological phenomena are concurrently manifest as changes across multiple clinical streams. Analysis of physiological signals is often more meaningful when presented along with situational context awareness which needs to be embedded into the development of continuous monitoring and predictive systems to ensure its effectiveness and robustness. Similarly, Bressan et al. These wrappers can provide a better control over the MapReduce code and aid in the source code development. Krish Krishnan, in Data Warehousing in the Age of Big Data, 2013. The use of a GUI also raises other interesting possibilities such as real time interaction and visualization of datasets. Based on the Hadoop platform, a system has been designed for exchanging, storing, and sharing electronic medical records (EMR) among different healthcare systems [56]. The focus of this section was to provide readers with insights into how by using a data-driven approach and incorporating master data and metadata, you can create a strong, scalable, and flexible data processing architecture needed for processing and integration of Big Data and the data warehouse. Advanced Multimodal Image-Guided Operating (AMIGO) suite has been designed which has angiographic X-ray system, MRI, 3D ultrasound, and PET/CT imaging in the operating room (OR). Special Issue on Computer Vision, Big Data and AI Research in Combating COVID-19. An aspect of healthcare research that has recently gained traction is in addressing some of the growing pains in introducing concepts of big data analytics to medicine. Another distribution technique involves exporting the data as flat files for use in other applications like web reporting and content management platforms. For instance, microscopic scans of a human brain with high resolution can require 66TB of storage space [32]. Hive is another MapReduce wrapper developed by Facebook [42]. However, this system is still in the design stage and cannot be supported by today’s technologies. MongoDB is a free cross-platform document-oriented database which eschews traditional table-based relational database. The most important step in creating the integration of Big Data into a data warehouse is the ability to use metadata, semantic libraries, and master data as the integration links. Electrocardiogrpah parameters from telemetry along with demographic information including medical history, ejection fraction, laboratory values, and medications have been used to develop an inhospital early detection system for cardiac arrest [116]. In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) The accuracy, sensitivity, and specificity were reported to be around 70.3%, 65.2%, and 73.7%, respectively. Most experts expect spending on big data technologies to continue at a breakneck pace through the rest of the decade. Processing Big Data has several substages, and the data transformation at each substage is significant to produce the correct or incorrect output. Part of my research focuses on algorithms and Markov random fields, a class of probabilistic model based on graphs used to capture dependencies in multivariate data (e.g., image models, data compression, computational biology). Map and Reduce functions are programmed by users to process the big data distributed across multiple heterogeneous nodes. Positron emission tomography (PET), CT, 3D ultrasound, and functional MRI (fMRI) are considered as multidimensional medical data. More importantly, adoption of insights gained from big data analytics has the potential to save lives, improve care delivery, expand access to healthcare, align payment with performance, and help curb the vexing growth of healthcare costs. Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. The entire structure is similar to the general model discussed in the previous section, consisting of a source, a cluster of processing nodes, and a sink. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined. Historical approaches to medical research have generally focused on the investigation of disease states based on the changes in physiology in the form of a confined view of certain singular modality of data [6]. challenge in fog-supported big data processing in disaster areas. As data intestine frameworks have evolved, there have been increasing amounts of higher-level APIs which are designed to further decrease the complexities of creating data intensive applications. There are variety of tools, but no “gold standard” for functional pathway analysis of high-throughput genome-scale data [138]. Explain how the maintenance of metadata is achieved. One example is iDASH (integrating data for analysis, anonymization, and sharing) which is a center for biomedical computing [55]. Reconstruction of metabolic networks has advanced in last two decades. MapReduce is the Hadoop's native batch processing engine. Several types of data need multipass processing and scalability is extremely important. Typically each health system has its own custom relational database schemas and data models which inhibit interoperability of healthcare data for multi-institutional data sharing or research studies. Boolean regulatory networks [135] are a special case of discrete dynamical models where the state of a node or a set of nodes exists in a binary state. Medical image analysis covers many areas such as image acquisition, formation/reconstruction, enhancement, transmission, and compression. The implementation and optimization of the MapReduce model in a distributed mobile platform will be an important research direction. Who maintains the metadata (e.g., Can users maintain it? With large volumes of streaming data and other patient information that can be gathered from clinical settings, sophisticated storage mechanisms of such data are imperative. Hadoop optimization based on multicore and high-speed storage devices. Various attempts at defining big data essentially characterize it as a collection of data elements whose size, speed, type, and/or complexity require one to seek, adopt, and invent new hardware and software mechanisms in order to successfully store, analyze, and visualize the data [1–3]. For example, employment agreements have standard and custom sections and the latter is ambiguous without the right context. Though linkage processing is the best technique known today for processing textual and semi-structured data, its reliance upon quality metadata and master data along with external semantic libraries proves to be a challenge. Gross, and M. Saeed, “Predicting icu hemodynamic instability using continuous multiparameter trends,” in, A. Smolinska, A.-Ch. This software is even available through some Cloud providers such as Amazon EMR [96] to create Hadoop clusters to process big data using Amazon EC2 resources [45]. AWS Cloud offers the following services and resources for Big Data processing [46]: Elastic Compute Cloud (EC2) VM instances for HPC optimized for computing (with multiple cores) and with extended storage for large data processing. All authors have read and approved the final version of this paper. Data standardization occurs in the analyze stage, which forms the foundation for the distribute stage where the data warehouse integration happens. This is an example of linking a customer’s electric bill with the data in the ERP system. To represent information detail in data, we propose a new concept called data resolution. However, the Spring XD is using another term called XD nodes to represent both the source nodes and processing nodes. The opportunity of addressing the grand challenge requires close cooperation among experimentalists, computational scientists, and clinicians. Data needs to be processed in parallel across multiple systems. Utilizing such high density data for exploration, discovery, and clinical translation demands novel big data approaches and analytics. As the size and dimensionality of data increase, understanding the dependencies among the data and designing efficient, accurate, and computationally effective methods demand new computer-aided techniques and platforms. An average of 33% improvement has been achieved compared to using only atlas information. This is discussed in the next section. The goal of iDASH is to bring together a multi-institutional team of quantitative scientists to develop algorithms and tools, services, and a biomedical cyber infrastructure to be used by biomedical and behavioral researchers [55]. Data of different types needs to be processed. Figure 11.7. Hence, the design of the access platform with high-efficiency, low-delay, complex data-type support becomes more challenging. The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Amazon also offers a number of public datasets; the most featured are the Common Crawl Corpus of web crawl data composed of over 5 billion web pages, the 1000 Genomes Project, and Google Books Ngrams. The next step after contextualization of data is to cleanse and standardize data with metadata, master data, and semantic libraries as the preparation for integrating with the data warehouse and other applications. With the customer email address we can always link and process the data with the structured data in the data warehouse. This Boolean model successfully captured the network dynamics for two different immunology microarray datasets. del Toro and Muller have compared some organ segmentation methods when data is considered as big data. Page, O. Kocabas, S. Ames, M. Venkitasubramaniam, and T. Soyata, “Cloud-based secure health monitoring: optimizing fully-homomorphic encryption for streaming algorithms,” in. Thus, understanding and predicting diseases require an aggregated approach where structured and unstructured data stemming from a myriad of clinical and nonclinical modalities are utilized for a more comprehensive perspective of the disease states. The exponential growth of the volume of medical images forces computational scientists to come up with innovative solutions to process this large volume of data in tractable timescales. This is where MongoDB and other document-based databases can provide high performance, high availability, and easy scalability for the healthcare data needs [102, 103]. Additionally, there is a factor of randomness that we need to consider when applying the theory of probability. New technological advances have resulted in higher resolution, dimension, and availability of multimodal images which lead to the increase in accuracy of diagnosis and improvement of treatment. Another example of a similar approach is Health-e-Child consortium of 14 academic, industry, and clinical partners with the aim of developing an integrated healthcare platform for European paediatrics [51]. Big Data processing involves steps very similar to processing data in the transactional or data warehouse environments. Harmonizing such continuous waveform data with discrete data from other sources for finding necessary patient information and conducting research towards development of next generation diagnoses and treatments can be a daunting task [81]. Big Data engineering is a specialisation wherein professionals work with Big Data and it requires developing, maintaining, testing, and evaluating big data solutions. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. 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Georgios Theodoropoulos, in, Software Architecture for Big Data and the Cloud, A Deep Dive into NoSQL Databases: The Use Cases and Applications, A Taxonomy and Survey of Stream Processing Systems, System Optimization for Big Data Processing, Hadoop becomes the most important platform for, Challenges in Storing and Processing Big Data Using Hadoop and Spark, Shaik Abdul Khalandar Basha MTech, ... Dharmendra Singh Rajput PhD, in, Deep Learning and Parallel Computing Environment for Bioengineering Systems, Resource Management in Big Data Processing Systems, Cloud Computing Infrastructure for Data Intensive Applications, Big Data Analytics for Sensor-Network Collected Intelligence, AWS Cloud offers the following services and resources for, Journal of Network and Computer Applications, Journal of Parallel and Distributed Computing. Our world has been facing unprecedented challenges as a result of the COVID-19 pandemic. An article focusing on neurocritical care explores the different physiological monitoring systems specifically developed for the care of patients with disorders who require neurocritical care [122]. Another option is to process the data through a knowledge discovery platform and store the output rather than the whole data set. Related image analysis and processing topics, such as dimensionality reduction; image compression; compressive sensing in big data analytics; content-based image retrieval; and MapReduce is proposed by Google and developed by Yahoo. Furthermore, each of these data repositories is siloed and inherently incapable of providing a platform for global data transparency. This similarity can potentially help care givers in the decision making process while utilizing outcomes and treatments knowledge gathered from similar disease cases from the past. Tsymbal et al. This is important because studies continue to show that humans are poor in reasoning about changes affecting more than two signals [13–15]. Big Data has been playing a role of a big game changer for most of the industries over the last few years. Recognizing the problem of transferring large amount of data to and from cloud, AWS offers two options for fast data upload, download, and access: (1) postal packet service of sending data on drive; and (2) direct connect service that allows the customer enterprise to build a dedicated high speed optical link to one of the Amazon datacenters [47]. We can classify Big Data requirements based on its five main characteristics: Size of data to be processed is large—it needs to be broken into manageable chunks. Linkage of different units of data from multiple data sets is not a new concept by itself. These techniques are among a few techniques that have been either designed as prototypes or developed with limited applications. The dynamical ODE model has been applied to reconstruct the cardiogenic gene regulatory network of the mammalian heart [158]. Historically streaming data from continuous physiological signal acquisition devices was rarely stored. Research in signal processing for developing big data based clinical decision support systems (CDSSs) is getting more prevalent [110]. New analytical frameworks and methods are required to analyze these data in a clinical setting. The variety of fixed as well as mobile sensors available for data mining in the healthcare sector and how such data can be leveraged for developing patient care technologies are surveyed in [127]. Moreover, it is utilized for organ delineation, identifying tumors in lungs, spinal deformity diagnosis, artery stenosis detection, aneurysm detection, and so forth. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Our research covers a broad range of topics related to the management and analysis of data. This is due to the customer data being present across both the systems. In these applications, image processing techniques such as enhancement, segmentation, and denoising in addition to machine learning methods are employed. Big data analytics which leverages legions of disparate, structured, and unstructured data sources is going to play a vital role in how healthcare is practiced in the future. As mentioned in previous section, big data usually stored in thousands of commodity servers so traditional programming models such as message passing interface (MPI) [40] cannot handle them effectively. Noise reduction, artifact removal, missing data handling, contrast adjusting, and so forth could enhance the quality of images and increase the performance of processing methods. Rep., Emory University, Atlanta, Ga, USA, 2011. In this chapter, we first make an overview of existing Big Data processing and resource management systems. The third generation includes pathway topology based tools which are publicly available pathway knowledge databases with detailed information of gene products interactions: how specific gene products interact with each other and the location where they interact [25]. MapReduce is a programming paradigm that provides scalability across many servers in a Hadoop cluster with a broad variety of real-world applications [44–46]. Initiatives are currently being pursued over the timescale of years to integrate clinical data from the genomic level to the physiological level of a human being [22, 23]. Different methods utilize different information available in experiments which can be in the form of time series, drug perturbation experiments, gene knockouts, and combinations of experimental conditions. The concept of “big data” is not new; however the way it is defined is constantly changing. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. A task-scheduling algorithm that is based on efficiency and equity. Telemetry and physiological signal monitoring devices are ubiquitous. Amazon DynamoDB highly scalable NoSQL data stores with submillisecond response latency. The first generation encompasses overrepresentation analysis approaches that determine the fraction of genes in a particular pathway found among the genes which are differentially expressed [25]. Big Data Analytic for Image processing. Similar to medical images, medical signals also pose volume and velocity obstacles especially during continuous, high-resolution acquisition and storage from a multitude of monitors connected to each patient. The stages and their activities are described in the following sections in detail, including the use of metadata, master data, and governance processes. Categorize—the process of categorization is the external organization of data from a storage perspective where the data is physically grouped by both the classification and then the data type. Determining connections in the regulatory network for a problem of the size of the human genome, consisting of 30,000 to 35,000 genes [16, 17], will require exploring close to a billion possible connections. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. The dynamics of gene regulatory network can be captured using ordinary differential equations (ODEs) [155–158]. It has both functional and physiological information encoded in the dielectric properties which can help differentiate and characterize different tissues and/or pathologies [37]. Without applying the context of where the pattern occurred, it is easily possible to produce noise or garbage as output. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray, molecular imaging, ultrasound, photoacoustic imaging, fluoroscopy, positron emission tomography-computed tomography (PET-CT), and mammography are some of the examples of imaging techniques that are well established within clinical settings.

big data image processing research areas

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