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

Abstract

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.

Machine and deep learning meet genome-scale metabolic modeling

“…This task can be conveniently tackled through machine learning algorithms, many of which can be adapted to specific settings and omic types. A number of recent developments in the application of machine learning to problems in molecular biology and biomedicine have been critically analyzed in previous surveys, along with their limitations and challenges [4][5][6][7][8][9][25][26][27]. Here, we concentrate on recalling the main characteristics of basic methods, with a focus on those suited for the simultaneous analysis of heterogeneous data.…”

Applying the exposome concept in birth cohort research: a review of statistical approaches

“…We give here a non-exhaustive list of specific tools developed for omics data that can be adapted to study omics in the context of exposome research depending on the research questions ( Fig. 1) [47,61,[69][70][71][72]. Network based approaches in systems biology and medicine, including transcription factor binding, protein-protein interactions, metabolic interactions, genetic interaction and disease-disease association (diseasome) networks, have helped to interpret the behavior of molecules or diseases that are related, and to provide insights into their mechanisms.…”

Editorial: Artificial Intelligence Bioinformatics: Development and Application of Tools for Omics and Inter-Omics Studies

“…Machine learning, statistical learning, and soft-computing approaches, such as deep neural networks or genetic algorithms, have also become terms used in the bio world, with an incomplete comprehension however, of their potential (Pavel et al, 2016;Lin and Lane, 2017;Zeng and Lumley, 2018). In recent years, omics, multi-omics, and inter-omics experiments have presented a further step toward the investigation in biology, opening the window on personalized medicine, for example for diagnostics (Riemenschneider et al, 2016).…”