Big Data Analytics In Bioinformatics And Healthcare Pdf


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As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information.

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Big Data in Health Care: Applications and Challenges

Partizanska bb, Bitola, Republic of Macedonia. This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various — omics data genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics , biomedical data and electronic health records data.

We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given. To obtain the best services and care for the patients, healthcare organizations in many countries have proposed various models of healthcare information systems.

These models for personalized, predictive, participatory and preventive medicine are based on using of electronic health records EHRs and huge amounts of complex biomedical data and high-quality — omics data [ 1 ].

Contemporarily genomics and postgenomics technologies produce huge amounts of raw data about complex biochemical and regulatory processes in the living organisms [ 2 ]. These -omics data are heterogeneous, and very often they are stored in different data formats. Similar to these - omics data, the EHRs data are also in heterogeneous formats. The EHRs data can be structured, semi-structured or unstructured; discrete or continuous. Big data in healthcare and medicine refers to these various large and complex data, which they are difficult to analyse and manage with traditional software or hardware [ 3 ], [ 4 ].

Big data analytics covers integration of heterogeneous data, data quality control, analysis, modeling, interpretation and validation [ 5 ]. Application of big data analytics provides comprehensive knowledge discovering from the available huge amount of data. Particularly, big data analytics in medicine and healthcare enables analysis of the large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 2 ].

Big data analytics in medicine and healthcare integrates analysis of several scientific areas such as bioinformatics, medical imaging, sensor informatics, medical informatics and health informatics. The new knowledge discovered by big data analytics techniques should provide comprehensive benefits to the patients, clinicians and health policy makers [ 7 ].

The remainder of the paper is organized as follows. Related work is described in the second section. Section 3 describes characteristics of big data, while big data analytics is depicted in the subsequent section. The next section explains some challenging issues about big data analytics techniques, while big data privacy and security are described in Section 6.

Last section concludes this paper with discussion and further works. The rapid development of the emerging information technologies, experimental technologies and methods, cloud computing, the Internet of Things, social networks supplies the amounts of generated data that is growing tremendously in numerous research fields [ 8 ].

On this point, contemporarily genomics and postgenomics technologies produce huge amounts of raw data about complex biochemical and regulatory processes in the living organisms [ 2 ]. These high throughput — omics data provide comprehensive insight towards different kinds of molecular profiles, changes and interactions, such as knowledge allied to the genome, epigenome, transcriptome, proteome, metabolome, interactome, pharmacogenome, diseasome, etc.

These — omics data are heterogeneous and very often stored in different data formats. The main aims and characteristics of the different — omics disciplines are tabled in Table 1. Similar to these — omics data, the EHRs data are also stored in heterogeneous formats. Some of these data are acquired from wearable sensors or capture from medical monitoring devices, with different collection frequency [ 5 ] that makes these data to have complex features and high dimensions [ 10 ].

Dealing with noisiness and incompleteness of EHRs are still challenging task and these shortcomings should be consider while applying data mining techniques [ 11 ]. These growing amounts of various — omics data need to be collect, clean, store, transform, transfer, visualize and deliver in a suitable manner to be represented to the clinicians [ 12 ]. The processing of these big data in medicine and healthcare can be accelerating by using cloud computing and powerful multicore central processing units CPUs , graphics processing units GPU and field-programmable gate arrays FPGAs with parallel processing methods.

The volume of health and medical data is expected to raise intensely in the years ahead, usually measured in terabytes, petabytes even yottabytes [ 14 ], [ 16 ]. Volume refers to the amount of data, while velocity refers to data in motion as well as and to the speed and frequency of data creation, processing and analysis. Complexity and heterogeneity of multiple datasets, which can be structured, semi-structured and unstructured, refer to the variety.

Veracity referrers to the data quality, relevance, uncertainty, reliability and predictive value [ 14 ], while variability regards about consistency of the data over time.

The value of the big data refers to their coherent analysis, which should be valuable to the patients and clinicians. Considering the big data characteristics, data searching, storage and analysis, a very appropriate and promising software platform for development of applications that can handle big data in medicine and healthcare is the open-source distributed data processing platform Apache Hadoop MapReduce [ 1 ], [ 17 ] that is based on data-intensive computing and NoSQL data modeling techniques [ 18 ].

Applications of big data analytics can improve the patient-based service, to detect spreading diseases earlier, generate new insights into disease mechanisms, monitor the quality of the medical and healthcare institutions as well as provide better treatment methods [ 19 ], [ 20 ], [ 21 ]. Data mining techniques employed on EHRs, web and social media data enable identifying the optimal practical guidelines in the hospitals, identifying the association rules in the EHRs [ 22 ] and revealing the disease monitoring and health-based trends.

Moreover, integration and analysis of the data with different nature, such as social and scientific, can lead to new knowledge and intelligence, exploring new hypothesis, identifying hidden patterns [ 14 ]. Nowadays, smart phones are excellent platforms to deliver personal messages to patients to involve them in behavioral changes to improve their wellbeing and health conditions.

The mobile phone messages can substitute delivering of medical and motivational advices to the patients [ 14 ]. Regarding collection of large amount data, some challenging issues should be considered. Obtaining high-throughput — omics data is tied to the cost of experimental measurements.

Concerning heterogeneity of the data sources, the noise of the experimental — omics data and the variety of the experimental techniques, environmental conditions, biological nature should be considered, before integration of these heterogeneous data and before employing of the data mining methods.

Different data mining techniques can be applied on these heterogeneous biomedical data sets, such as: anomaly detection, clustering, classification, association rules as well as summarization and visualization of those big data sets. These shortcomings might lead to the unreliability of some of the data points, such as missing values or outliers. Integration of data from various databases and standardization for laboratory protocols and values still remain challenging issues [ 10 ].

The subsequent stage is the pre-processing of the data, which usually envelop handling noisy data, outliers, missing values, data transformation and normalization. This data pre-processing enables to be applied statistical techniques and data mining methods and thus the big data analytics quality and outcomes can improve and can result with discovering of novel knowledge.

This novel knowledge obtained by integration of the — omics and EHRs data should results with improving of the implemented healthcare to the patients as well to advanced decision making by the healthcare decision policy makers. All medical data are very sensitive and different countries consider these data as legally possessed by the patients [ 2 ]. To address these security and privacy challenges, the big data analytics software solutions should use advanced encryption algorithms and pseudo-anonymization of the personal data.

These software solutions should provide security on the network level and authentication for all involved users, guarantee privacy and security, as well as set up good governance standards and practices. Big data analytics in medicine and healthcare is very promising process of integrating, exploring and analysing of large amount complex heterogeneous data with different nature: biomedical data, experimental data, electronic health records data and social media data.

Integration of such diverse data makes big data analytics to intertwine several fields, such as bioinformatics, medical imaging, sensor informatics, medical informatics, health informatics and computational biomedicine.

As a further work, the big data characteristics provide very appropriate basis to use promising software platforms for development of applications that can handle big data in medicine and healthcare.

One such platform is the open-source distributed data processing platform Apache Hadoop MapReduce that use massive parallel processing MPP [ 20 ], [ 24 ]. These applications should enable applying data mining techniques to these heterogeneous and complex data to reveal hidden patterns and novel knowledge from the data. Authors state no conflict of interest. National Center for Biotechnology Information , U. Journal List J Integr Bioinform v. J Integr Bioinform. Published online May Blagoj Ristevski and Ming Chen.

China Find articles by Ming Chen. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Blagoj Ristevski: km. This article has been cited by other articles in PMC. Abstract This paper surveys big data with highlighting the big data analytics in medicine and healthcare.

Introduction To obtain the best services and care for the patients, healthcare organizations in many countries have proposed various models of healthcare information systems. Related Work The rapid development of the emerging information technologies, experimental technologies and methods, cloud computing, the Internet of Things, social networks supplies the amounts of generated data that is growing tremendously in numerous research fields [ 8 ].

Table 1: The main aims of the variety of — omics disciplines. Open in a separate window. Figure Big Data Analytics Applications of big data analytics can improve the patient-based service, to detect spreading diseases earlier, generate new insights into disease mechanisms, monitor the quality of the medical and healthcare institutions as well as provide better treatment methods [ 19 ], [ 20 ], [ 21 ]. Challenges in Big Data Analytics Regarding collection of large amount data, some challenging issues should be considered.

Discussion and Future Work Big data analytics in medicine and healthcare is very promising process of integrating, exploring and analysing of large amount complex heterogeneous data with different nature: biomedical data, experimental data, electronic health records data and social media data. Conflict of interest statement Authors state no conflict of interest. Implications of pleiotropy: challenges and opportunities for mining big data in biomedicine.

Front Genet. Big data, big knowledge: big data for personalized healthcare. Big data and analytics in healthcare: introduction to the special section. Inform Syst Front. Big data analytics in healthcare: promise and potential.

Health Inform Sci Syst. An integrated big data analytics-enabled transformation model: application to health care. Inf Manag. Opportunities for business intelligence and big data analytics in evidence based medicine. Visualizing the knowledge structure and evolution of big data research in healthcare informatics.

Int J Med Inform. Integrative methods for analyzing big data in precision medicine. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights. The effectiveness of big data in health care: a systematic review. In: Metadata and semantics research.

Impact of Big Data Analytics in Bioinformatics: A Literature Review

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Impact of Big Data Analytics in Bioinformatics: A Literature Review

This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various — omics data genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics , biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.

Metrics details. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things.

Partizanska bb, Bitola, Republic of Macedonia. This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various — omics data genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics , biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security.

The concept of Big Data is popular in a variety of domains. The purpose of this review was to summarize the features, applications, analysis approaches, and challenges of Big Data in health care.

Big Data Analytics in Medicine and Healthcare

 Хочешь со мной переспать? - Теперь на Беккера смотрела юная девица, похожая на персонаж фильма ужасов Рассвет мертвецов. Темнота коридора перетекла в просторное цементное помещение, пропитанное запахом пота и алкоголя, и Беккеру открылась абсолютно сюрреалистическая картина: в глубокой пещере двигались, слившись в сплошную массу, сотни человеческих тел. Они наклонялись и распрямлялись, прижав руки к бокам, а их головы при этом раскачивались, как безжизненные шары, едва прикрепленные к негнущимся спинам. Какие-то безумцы ныряли со сцены в это людское море, и его волны швыряли их вперед и назад, как волейбольные мячи на пляже.

 Грег, - сказала она, и голос ее зазвучал мягче, хотя далось ей это нелегко.  - Сегодня я не в духе. Меня огорчают твои разговоры о нашем агентстве как каком-то соглядатае, оснащенном современной техникой. Эта организация создавалась с единственной целью - обеспечивать безопасность страны. При этом дерево иногда приходится потрясти, чтобы собрать подгнившие плоды. И я уверена, что большинство наших граждан готовы поступиться некоторыми правами, но знать, что негодяи не разгуливают на свободе. Хейл промолчал.

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Метрах в пятистах сзади в снопе искр на шоссе выкатило такси. Набирая скорость, оно столкнуло в сторону Пежо-504, отбросив его на газон разделительной полосы. Беккер миновал указатель Центр Севильи - 2 км. Если бы ему удалось затеряться в центральной части города, у него был бы шанс спастись. Спидометр показывал 60 миль в час. До поворота еще минуты две.

 - Вы дежурили все это время. - Моя смена от семи до семи, - кивнула женщина. - Тогда вы наверняка ее видели. Это совсем молоденькая девушка. Лет пятнадцати-шестнадцати. Волосы… - Не успев договорить, он понял, что совершил ошибку.

Big data in healthcare: management, analysis and future prospects

Тебе он всегда рад.

 Расскажи.  - Она надулась.  - Если не скажешь, тебе меня больше не видать.

Тот огляделся вокруг, указательным пальцем разгладил усы и наконец заговорил: - Что вам нужно? - Он произносил английские слова немного в нос. - Сэр, - начал Беккер чуть громче, словно обращаясь к глуховатому человеку, - я хотел бы задать вам несколько вопросов. Старик посмотрел на него с явным недоумением. - У вас какие-то проблемы.

Двадцать миллионов долларов - это очень большие деньги, но если принять во внимание, за что они будут заплачены, то это сущие гроши. ГЛАВА 19 - А вдруг кто-то еще хочет заполучить это кольцо? - спросила Сьюзан, внезапно заволновавшись.  - А вдруг Дэвиду грозит опасность.

 - Пожалуйста. Через десять минут Беккер уже сидел в буфете АНБ, жуя сдобную булку и запивая ее клюквенным соком, в обществе очаровательной руководительницы Отделения криптографии АНБ.

3 Comments

Abby L.
22.04.2021 at 22:47 - Reply

Download Citation | Big data analytics in bioinformatics and healthcare | As technology evolves and electronic data becomes Request Full-text Paper PDF.

Bilbandruhmett
26.04.2021 at 06:25 - Reply

Also, the healthcare is one of the world's largest and fastest growing industries [​]. In health sciences, big data in many fields such as.

Phillip S.
30.04.2021 at 05:40 - Reply

Analytics in healthcare came as a result of large healthcare data that are being gathered electronically. Data analytics is proficient in terms of healthcare.

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