Rejoinder to ‘Statistical learning methods for information security: fundamentals and case studies’

2015 ◽  
Vol 31 (2) ◽  
pp. 119-121 ◽  
Author(s):  
Hsing-Kuo Pao ◽  
Yuh-Jye Lee ◽  
Chun-Ying Huang
Keyword(s):  
Information Security ◽  
Case Studies ◽  
Statistical Learning ◽  
Learning Methods

Multicopter Fault Detection and Identification via Data-Driven Statistical Learning Methods

AIAA Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Airin Dutta ◽  
Michael E. McKay ◽  
Fotis Kopsaftopoulos ◽  
Farhan Gandhi
Keyword(s):  
Fault Detection ◽  
Statistical Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Detection And Identification ◽  
Fault Detection And Identification

scholarly journals Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

2018 ◽  
Vol 12 ◽  
pp. 117793221875929 ◽  
Author(s):  
Irene Sui Lan Zeng ◽  
Thomas Lumley
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
Information Science ◽  
Molecular System ◽  
Principal Component ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Integrated Omics ◽  
Types Of Information ◽  
Integrated Information

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

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