Data Analytics for Longitudinal Biomedical Data

2020 ◽  
pp. 257-260
Author(s):  
Hua Fang
Keyword(s):  
Data Analytics ◽  
Biomedical Data

Biomedical data analytics in mobile-health environments for high-risk pregnancy outcome prediction

2019 ◽  
Vol 10 (10) ◽  
pp. 4121-4134 ◽  
Author(s):  
Mário W. L. Moreira ◽  
Joel J. P. C. Rodrigues ◽  
Francisco H. C. Carvalho ◽  
Naveen Chilamkurti ◽  
Jalal Al-Muhtadi ◽  
...  
Keyword(s):  
High Risk ◽  
Pregnancy Outcome ◽  
Mobile Health ◽  
Data Analytics ◽  
Outcome Prediction ◽  
High Risk Pregnancy ◽  
Biomedical Data ◽  
Risk Pregnancy

The age of data analytics: converting biomedical data into actionable insights

Methods ◽  
2018 ◽  
Vol 151 ◽  
pp. 1-2 ◽  
Author(s):  
Kirill Veselkov ◽  
Bjoern Schuller
Keyword(s):  
Data Analytics ◽  
Biomedical Data

scholarly journals A Novel Customized Big Data Analytics Framework for Drug Discovery

2018 ◽  
Author(s):  
A. Jainul Fathima ◽  
G. Murugaboopathi
Keyword(s):  
Big Data ◽  
Drug Discovery ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Pharmaceutical Companies ◽  
Biomedical Data ◽  
Analytic Framework ◽  
Computational Tools ◽  
Computational Drug Discovery ◽  
Insight Into

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.  

scholarly journals Temporal biomedical data analytics

2019 ◽  
Vol 90 ◽  
pp. 103092 ◽  
Author(s):  
Robert Moskovitch ◽  
Yuval Shahar ◽  
Fei Wang ◽  
George Hripcsak
Keyword(s):  
Data Analytics ◽  
Biomedical Data

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

2021 ◽  
pp. 135-163
Author(s):  
Heng Wang ◽  
Yang Song ◽  
Zihao Tang ◽  
Chaoyi Zhang ◽  
Jianhui Yu ◽  
...  
Keyword(s):  
Data Analytics ◽  
Biomedical Data ◽  
Neuronal Structure ◽  
Structure Reconstruction

scholarly journals Big Data Analytics in Medicine and Healthcare

2018 ◽  
Vol 15 (3) ◽  
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.

scholarly journals Platform for Big Biomedical Data Streams Management and Analytics

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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