Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods

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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Effective Statistical Learning Methods for Actuaries I

10.1007/978-3-030-25820-7 ◽

2019 ◽

Cited By ~ 9

Author(s):

Michel Denuit ◽

Donatien Hainaut ◽

Julien Trufin

Keyword(s):

Statistical Learning ◽

Learning Methods

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Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology

JCO Precision Oncology ◽

10.1200/po.19.00018 ◽

2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Yize Zhao ◽

Changgee Chang ◽

Qi Long

Keyword(s):

Statistical Learning ◽

Current Knowledge ◽

Complex Diseases ◽

High Dimensional ◽

Future Research ◽

Great Promise ◽

Biological Knowledge ◽

Precision Oncology ◽

Omics Data ◽

Learning Methods

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of complex diseases such as cancer at an unprecedented scale and in multiple dimensions. However, a number of analytical challenges complicate analysis of high-dimensional -omics data. One is the growing recognition that complex diseases such as cancer are multifactorial and may be attributed to harmful changes on multiple -omics levels and on the pathway level. When individual genes in an important pathway have relatively weak signals, it can be challenging to detect them on their own, but the aggregated signal in the pathway can be considerably stronger and hence easier to detect with the same sample size. To address these challenges, there is a growing body of literature on knowledge-guided statistical learning methods for analysis of high-dimensional -omics data that can incorporate biological knowledge such as functional genomics and functional proteomics. These methods have been shown to improve predication and classification accuracy and yield biologically more interpretable results compared with statistical learning methods that do not use biological knowledge. In this review, we survey current knowledge-guided statistical learning methods, including both supervised learning and unsupervised learning, and their applications to precision oncology, and we discuss future research directions.

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Improving moving average trading rules with boosting and statistical learning methods

Journal of Forecasting ◽

10.1002/for.1068 ◽

2008 ◽

Vol 27 (5) ◽

pp. 433-449 ◽

Cited By ~ 22

Author(s):

Julián Andrada-Félix ◽

Fernando Fernández-Rodríguez

Keyword(s):

Statistical Learning ◽

Moving Average ◽

Learning Methods ◽

Trading Rules

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