Big Data Analytics and Deep Learning in Bioinformatics With Hadoop

Armoogum, Sandhya; Li, Xiaoming

doi:10.1016/b978-0-12-816718-2.00009-9

Cited by 19 publications

(5 citation statements)

References 46 publications

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“…Literature on applying big data analytics in the healthcare industry revealed that big data analytics technologies could be useful in clinical decision support, personalized medicine and disease surveillance (2,13,(23)(24)(25)(26). Big data analytics techniques can be integrated into different healthcare domains including medical image analysis and imaging informatics (43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53), population health management (60-64), clinical informatics (66-68) and bioinformatics (72,74,77). Integrating big data technologies in healthcare can enhance the quality of care and identify high-risk patients early through real-time analytics, which can optimize clinical operations and save lives at a lower cost (3)(4)(5)(6).…”

Section: Discussionmentioning

confidence: 99%

“…Bioinformatics is an interdisciplinary area involving molecular biology, computer science, mathematics and statistics, with a focus on computational analysis of biological datasets such as genomics, proteomics, transcriptomics, metabolomics and pharmacogenomics (69, 72). The eld of bioinformatics has been widely used to understand disease processes at a molecular level, enabling disease risk strati cation and early detection, development of individualized treatment strategies, and predicting treatment outcomes and patient response to therapeutic interventions (69, [72][73][74]. The eld of bioinformatics involves the analysis of a huge volume of data which has been growing drastically (74).…”

Section: Bioinformaticsmentioning

confidence: 99%

“…For example, a single sequenced human genome is approximately 200 gigabytes (75). Although the availability of this huge volume of data is essential for accurate analytics (74), this huge volume of biological data also poses analytical challenges (72,74), especially in developing countries where big data analytics and cloud computing tools have not been effectively integrated. Furthermore, the heterogeneous nature as well as the ethnic and geographical variations of these data increase the complexity of their analysis ( 74), further minimizing their effective utilization.…”

Section: Bioinformaticsmentioning

confidence: 99%

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Big Data Analytics in the Healthcare Sector: Opportunities and Challenges in Developing Countries. A Literature Review

Muhunzi

Kitambala

Mashauri

2023

Preprint

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Background: The availability of vast amounts of healthcare data, along with the advancements in big data analytics techniques, has led to an unprecedented opportunity for healthcare organizations to improve patient outcomes and reduce healthcare costs. Despite the ongoing digitization of healthcare sector in developing countries, the full adoption of big data analytics in healthcare and the effective realization of its potential benefits still need to be reached. Exploring opportunities and challenges encountered in implementing data analytics in healthcare in developing countries is essential for designing implementation strategies and interventions. Objective:Exploring opportunities and challenges towards integrating big data analytics technologies in the healthcare industry in developing countries. Methodology: A literature search was conducted in PubMed, Science Direct and Google Scholar with the keywords "big data analytics", "big data analytics healthcare", "big data analytics healthcare developing countries", "healthcare data analytics", and "healthcare data analytics developing countries". Articles with predetermined keywords and written in English were included in the review. Results:Big data analytics finds its application in medical images analysis and imaging informatics, population health management, clinical informatics and bioinformatics, as decision support algorithms even in developing countries. The major challenges to the clinical adoption of big data analytics in healthcare in developing countries include the need for more evidence for its practical benefits, competing priorities, the lack of trained personnel for analyzing big healthcare data and the lack of substantial financial investment required. Conclusion:Integrating big data analytics in developing countries’ healthcare industry can potentially open new avenues for enhancing healthcare delivery. Despite the benefits big data analytics can have in the healthcare industry of developing countries, highlighted strategies should be implemented to address several challenges encountered towards using this technology.

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Section: Discussionmentioning

confidence: 99%

Section: Bioinformaticsmentioning

confidence: 99%

Section: Bioinformaticsmentioning

confidence: 99%

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Big Data Analytics in the Healthcare Sector: Opportunities and Challenges in Developing Countries. A Literature Review

Muhunzi

Kitambala

Mashauri

2023

Preprint

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“…In 2014, as an Apache incubator project, Stratosphere became an open-source project with the name "Flink". Flink is known to process data hundred times faster than MapReduce [55]. Due to its highly flexible windowing mechanism, Flink programs can calculate early and approximate results, as well as delayed and accurate results through the same process, so there is no need to combine different systems for the two use cases [56].…”

Section: Flinkmentioning

confidence: 99%

A Comparative Analysis of Big Data Frameworks: An Adoption Perspective

Khalid

Yousaf

2021

Applied Sciences

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The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground for forthcoming frameworks that improve the processing and development of large-scale data in many ways. This research focuses on comparing the most prominent and widely used frameworks in the open-source landscape. We identify key requirements of a big framework and review each of these frameworks in the perspective of those requirements. To enhance the clarity of comparison and analysis, we group the logically related features, forming a feature vector. We design seven feature vectors and present a comparative analysis of frameworks with respect to those feature vectors. We identify use cases and highlight the strengths and weaknesses of each framework. Moreover, we present a detailed discussion that can serve as a decision-making guide to select the appropriate framework for an application.

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“…Such systems are capable of learning pattern based on some set of rules used in training dataset with a view to execute dedicated tasks [28]. ANN works on fundamental principles of central and peripheral nervous systems such that it observes, learns and processes patterns from datasets with sole aim to make decisions on an entirely new set of data [29], [30].…”

Section: Ann-based Fault Analysismentioning

confidence: 99%

Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study

et al. 2021

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The occurrence of faults in any operational power system network is inevitable, and many of the causative factors such as lightning, thunderstorm among others is usually beyond human control. Consequently, there is the need to set up models capable of prompt identification and classification of these faults for immediate action. This paper, explored the use of artificial neural network (ANN) technique to identify and classify various faults on the 11 kV distribution network of University of Lagos. The ANN is applied because it offers high speed, higher efficiency and requires less human intervention. Datasets of the case study obtained were sectioned proportionately for training, testing, and validation. The mathematical formulations for the method are presented with python used as the programming tools for the analysis. The results obtained from this study, for both the voltage and current under different scenarios of faults, are displayed in graphical forms and discussed. The results showed the effectiveness of the ANN in fault identification and classification in a distribution network as the model yielded satisfactory results for the available limited datasets used. The information obtained from this study could be helpful to the system operators in faults identification and classification for making informed decisions regarding power system design and reliability.

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Big Data Analytics and Deep Learning in Bioinformatics With Hadoop

Cited by 19 publications

References 46 publications

Big Data Analytics in the Healthcare Sector: Opportunities and Challenges in Developing Countries. A Literature Review

Big Data Analytics in the Healthcare Sector: Opportunities and Challenges in Developing Countries. A Literature Review

A Comparative Analysis of Big Data Frameworks: An Adoption Perspective

Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study

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