Machine learning and systems genomics approaches for multi-omics data

Abstract

In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

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).…”

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

“…In the field of precision psychiatry, researchers integrate multiple data types such as multi-omics and neuroimaging data with state-of-the-art artificial intelligence and machine learning algorithms, which can accordingly learn to identify complex patterns with respect to observational datasets [5][6][7]. Namely, multi-omics and neuroimaging data are employed to serve as biomarkers (or predictive factors) to fulfill the concept of precision psychiatry by using artificial intelligence and machine learning algorithms.…”

“…Namely, multi-omics and neuroimaging data are employed to serve as biomarkers (or predictive factors) to fulfill the concept of precision psychiatry by using artificial intelligence and machine learning algorithms. In order to address the demanding challenges we face today in the field of precision psychiatry, there is an enormous need for developing software tools in artificial intelligence and machine learning frameworks that can predict specific quantitative and/or categorical phenotypes in clinical settings by utilizing next-generation multi-omics and neuroimaging datasets [5][6][7].…”

“…In recent times, scientists have been making significant progress in the multidisciplinary fields of artificial intelligence, machine learning, precision psychiatry, pharmacogenomics, multi-omics, and neuroimaging [5][6][7]. The goal of an artificial intelligence and machine learning approach is to provide a data-driven algorithm that can in general learn from the data in the past and/or in the present by leveraging the learned insight into estimating predictive outcomes for any unknown data and/or for any unknown event in the future [8][9][10].…”

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

“…The aim of this study is to develop and apply a BN classifier that predicts NP-induced cellular effects using data inputs addressing NP physicochemical properties, experimental exposure conditions in vitro characteristics and transcriptomics outcomes. The study data gathered for output use functional analysis of the altered genes; investigating which biological pathways are most disrupted after exposure to a test substance (R€ omer et al 2014;McDermottet al 2013;Lin and Lane 2017). This represents a growing field within toxicology because of its capacity to improve risk assessment (Shukla et al 2010;Buesen et al 2017;Tsiliki, Nymark, Kohonen, Grafstr€ om and Sarimveis 2017;Franceschi et al 2017).…”