Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

• Published in: Processes Vol. 8; no. 8; p. 951
• Main Authors: He, Q. Peter, Wang, Jin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020; MDPI
• Subjects: Algorithms; Big Data; biological big data; Biology; Biomarkers; Biomedical data; Data analysis; Discriminant analysis; dynamic analysis; ENGINEERING; feature engineering; Health care; Learning algorithms; Life sciences; Machine learning; Mathematical analysis; MATHEMATICS AND COMPUTING; overfitting; Principles; Regression analysis; Reviews; Systems engineering
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr8080951

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.