An Introduction to Systems Analytics and Integration of Big Omics Data

Hardiman, Gary

doi:10.3390/genes11030245

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…In particular, the problem of computational burden is obvious, and it often takes hours or even days to analyze big medical data [ 8 ]. In response to this problem, researchers proposed that parallel processing of existing data can save processing time and increase the efficiency of medical data analysis [ 9 ]. However, this method will produce corresponding message transmission overhead.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Huang¹,

Dang

2022

Journal of Healthcare Engineering

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This study explores the risk factors of chronic pulmonary heart disease (CPHD) induced by plateau chronic obstructive pulmonary disease (COPD) based on intelligent medical treatment and big data of electrocardiogram (ECG) signal. Based on GPU, a wavelet algorithm is introduced to extract features of ECG signal, and it was combined with generalized regression neural network (GRNN) to improve classification accuracy. From June 2018 to December 2020, 10,185 patients diagnosed with COPD in the plateau area by pulmonary function testing, ECG, and chest X-ray at X Hospital are taken as the research objects to evaluate the distribution of CPHD incidence at different ages and altitudes. The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. Accuracy (Acc), sensitivity (Se), specificity (Sp), and positive rate (PR) of V were 99.03%, 89.17%, 98.92%, and 93.18%, respectively. The Acc, Se, Sp, and PR of S were 99.54%, 86.22%, 99.74%, and 92.56%, respectively. The GRNN classification accuracy was up to 98%. 19% of COPD patients were diagnosed with CPHD, including 1,409 males (72.82%) and 526 females (36.24%). The highest prevalence of CPHD was 64.60% when the altitude was 1,900–2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61–70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas.

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Section: Introductionmentioning

confidence: 99%

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Huang¹,

Dang

2022

Journal of Healthcare Engineering

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“…For more detailed guidelines and relevant method comparisons, we refer the reader to a broader overview of machine learning methods for omics data integration [ 45 ], representative case studies on combining omics and clinical data [ 46 ], and generic multi-omics integration approaches [ 47 , 48 ].…”

Section: Tip 3: Integrate Different Data Types Effectively and Assess...mentioning

confidence: 99%

Ten quick tips for biomarker discovery and validation analyses using machine learning

et al. 2022

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“…Hardiman has explored BDI methodologies for Omics data and network algorithm development [31]. The objective was to channel the gap between phenotype and genotype which were not applied earlier.…”

Section: Review Of Healthcare Sectormentioning

confidence: 99%

Big Data Integration Solutions in Organizations: A Domain-Specific Analysis

Karanam¹,

Kamath²,

Kulkarni³

et al. 2021

Data Integrity and Quality

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Big Data Integration (BDI) process integrates the big data arising from many diverse data sources, data formats presents a unified, valuable, customized, holistic view of data. BDI process is essential to build confidence, facilitate high-quality insights and trends for intelligent decision making in organizations. Integration of big data is a very complex process with many challenges. The data sources for BDI are traditional data warehouses, social networks, Internet of Things (IoT) and online transactions. BDI solutions are deployed on Master Data Management (MDM) systems to support collecting, aggregating and delivering reliable information across the organization. This chapter has conducted an exhaustive review of BDI literature and classified BDI applications based on their domain. The methods, applications, advantages and disadvantage of the research in each paper are tabulated. Taxonomy of concepts, table of acronyms and the organization of the chapter are presented. The number of papers reviewed industry-wise is depicted as a pie chart. A comparative analysis of curated survey papers with specific parameters to discover the research gaps were also tabulated. The research issues, implementation challenges and future trends are highlighted. A case study of BDI solutions implemented in various organizations was also discussed. This chapter concludes with a holistic view of BDI concepts and solutions implemented in organizations.

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An Introduction to Systems Analytics and Integration of Big Omics Data

Abstract: A major technological shift in the research community in the past decade has been the adoption of high throughput (HT) technologies to interrogate the genome, epigenome, transcriptome, and proteome in a massively parallel fashion [...]

Cited by 8 publications

References 15 publications

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Ten quick tips for biomarker discovery and validation analyses using machine learning

Big Data Integration Solutions in Organizations: A Domain-Specific Analysis

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