AI-Enhanced 3D Biomedical Data Analytics for Neuronal Structure Reconstruction

Drug discovery is related to analytics as the method requires a technique to handle the extremely large volume of structured and unstructured biomedical data of multi-dimensional and complexity from pharmaceutical companies. To tackle the complexity of data and to get better insight into the data, big data analytics can be used to integrate the massive amount of pharmaceutical data and computational tools in an analytic framework. This paper presents an overview of big data analytics in the field of drug discovery and outlines an analytic framework which can be applied to computational drug discovery process and briefly discuss the challenges. Hence, big data analytics may contribute to better drug discovery.

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Temporal biomedical data analytics

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2018.12.006 ◽

2019 ◽

Vol 90 ◽

pp. 103092 ◽

Cited By ~ 1

Author(s):

Robert Moskovitch ◽

Yuval Shahar ◽

Fei Wang ◽

George Hripcsak

Keyword(s):

Data Analytics ◽

Biomedical Data

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Data Analytics for Longitudinal Biomedical Data

Encyclopedia of Wireless Networks ◽

10.1007/978-3-319-32903-1_153-1 ◽

2020 ◽

pp. 1-4

Author(s):

Hua Fang

Keyword(s):

Data Analytics ◽

Biomedical Data

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Novel Incremental Ranking Framework for Biomedical Data Analytics and Dimensionality Reduction: Big Data Challenges and Opportunities

Journal of Computer Science & Systems Biology ◽

10.4172/jcsb.1000190 ◽

2015 ◽

Vol 8 (4) ◽

Cited By ~ 1

Keyword(s):

Big Data ◽

Dimensionality Reduction ◽

Data Analytics ◽

Biomedical Data ◽

Challenges And Opportunities

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Big Data Analytics in Medicine and Healthcare

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0030 ◽

2018 ◽

Vol 15 (3) ◽

Cited By ~ 38

Author(s):

Blagoj Ristevski ◽

Ming Chen

Keyword(s):

Big Data ◽

Data Analytics ◽

Data Privacy ◽

Big Data Analytics ◽

Heterogeneous Data ◽

Distributed Data ◽

Biomedical Data ◽

Privacy And Security ◽

Distributed Data Processing ◽

Data Processing Software

Abstract 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.

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Platform for Big Biomedical Data Streams Management and Analytics

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2020.14.74 ◽

2020 ◽

Vol 14 ◽

Keyword(s):

Data Storage ◽

Data Streams ◽

Data Analytics ◽

Large Scale ◽

Biomedical Data ◽

Computational Process ◽

Data Discovery ◽

Development Environment ◽

Scientific Experiments ◽

Integration Data

Major challenge in the analysis of clinical data and knowledge discovery is to suggest an integrated, advanced and efficient tools, methods and technologies for access and processing of progressively increasing amounts of data in multiple formats. The paper presents a platform for multidimensional large-scale biomedical data management and analytics, which covers all phases of data discovery, data integration, data preprocessing, data storage, data analytics and visualization. The goal is to suggest an intelligent solution as integrated, scalable workflow development environment consisting of a suite of software tools to automate the computational process in conducting scientific experiments.

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