Using machine learning approaches for multi-omics data analysis: A review

Reel, Parminder Singh; Reel, Smarti; Pearson, Ewan R.; Trucco, Emanuele; Jefferson, Emily

doi:10.1016/j.biotechadv.2021.107739

Cited by 385 publications

(251 citation statements)

References 309 publications

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“…[ 129,130 ] (b) ML is an effective tool for merging and analyzing heterogeneous omics datasets beyond ML applications to single omics. [ 131 ] By combining these multi‐omics datasets with GEMs, context‐specific models are generated. More accurate flux values obtained from context‐specific GEMs can be re‐integrated with experimental omics data for further predictions (Figure 5B).…”

Section: Synergisms Of Cbm and MLmentioning

confidence: 99%

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Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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Section: Synergisms Of Cbm and MLmentioning

confidence: 99%

“…This target is restricted due to the heterogeneous and high‐dimensional datasets. [ 126,131 ] Genomics and transcriptomics datasets are generated on different experimental platforms from proteomics and metabolomics datasets. Moreover, fluxomics data are produced in silico based on prior biological knowledge.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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“…Machine learning methods are already being developed to integrate data from multi-omics studies, for example including genetics, proteomics, and metabolomics to discover new biomarkers for disease. 98 Deep learning applications have traditionally been limited by hardware requirements. Modern deep learning is typically heavily dependent on graphics processing units (GPUs), which enable thousands of calculations to progress simultaneously at great speed.…”

Section: Variant Interpretation and Clinical Reportingmentioning

confidence: 99%

Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice

Wong

Olivera

et al. 2021

MAIO

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Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings.Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals.Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration.Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns.Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams.

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“…The limited number of samples that can be collected are usually noisy, incompletely annotated, sparse, and high-dimensional (many variables), making it very challenging to develop integrative computational approaches with regard to this type of data. Nowadays, several machine learning approaches have been proposed to analyze multi-omics datasets [ 2 ]. Specifically, unsupervised approaches learn representations by identifying patterns in the data and extracting meaningful knowledge, while overcoming data complexities.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Pratella

Saadi

Bannwarth

et al. 2021

IJMS

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Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.

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Using machine learning approaches for multi-omics data analysis: A review

Cited by 385 publications

References 309 publications

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

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