Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Čuperlović-Culf, Miroslava

doi:10.3390/metabo8010004

Cited by 129 publications

(91 citation statements)

References 88 publications

(92 reference statements)

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“…Metabolic networks. Machine learning has been applied to take advantage of large amounts of genomics and metabolomics data for the optimization of ordinary differential equation-based metabolic network models and their analysis [25]. For example, machine learning and genome-scale models were applied to determine the side effects of drugs [114].…”

Section: Applications and Opportunitiesmentioning

confidence: 99%

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng

Alber

Tepole³

et al. 2020

Arch Computat Methods Eng

231

103

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Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. This review serves as introduction to a special issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems that will help identify current roadblocks and areas where computational mechanics, as a discipline, can play a significant role. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

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Section: Applications and Opportunitiesmentioning

confidence: 99%

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng

Alber

Tepole³

et al. 2020

Arch Computat Methods Eng

231

103

View full text Add to dashboard Cite

show abstract

“…This information has a potential for greater impact on translational biomedicine. Furthermore, novel machine‐learning approaches are already available to study epigenetics and metabolic pathways . The advancements in NGS and the power of machine learning are underutilized in orthodontics.…”

Section: Implications For Orthodonticsmentioning

confidence: 99%

“…Furthermore, novel machine-learning approaches are already available to study epigenetics and metabolic pathways. 31,32 The advancements in NGS and the power of machine learning are underutilized in orthodontics. Future studies employing these technologies will create a paradigm shift in orthodontic diagnosis.…”

Section: Omics and Orthodonticsmentioning

confidence: 99%

Orthodontics in the era of big data analytics

Allareddy

Venugopalan

Nalliah

et al. 2019

Orthod Craniofacial Res

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Structured Abstract The objective of this report was to provide an overview of the current landscape of big data analytics in the healthcare sector, introduce various approaches of machine learning and discuss potential implications in the field of orthodontics. With the increasing availability of data from various sources, the traditional analytical methods may not be conducive anymore for examining clinical outcomes. Machine‐learning approaches, which are algorithms trained to identify patterns in large data sets, are ideally suited to facilitate data‐driven decision making. The field of orthodontics is particularly ripe for embracing the big data analytics platform to improve decision making in clinical practice. The availability of omics data, state‐of‐the‐art imaging and potential for establishing large clinical data repositories have favourably positioned the specialty of orthodontics to deliver personalized and precision orthodontic care. Specifically, we discuss about next‐generation sequencing, radiomics in the context of CBCT imaging, and how centralized data repositories can enable real‐time data pooling from multiple sources.

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“…In addition, generating theoretical spectra based on knowledge of metabolic pathways and allowing in silico inclusion of modifications to existing spectra (e.g., oxidation, hydroxylation and carboxylation) could enhance spectral matching success. Finally, application of machine learning for feature classification appears to be a promising future direction (Cuperlovic‐Culf, ).…”

Section: Big Wishes: a Wish List For Plant Researchmentioning

confidence: 99%

Directions for research and training in plant omics: Big Questions and Big Data

et al. 2019

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A key remit of the NSF ‐funded “Arabidopsis Research and Training for the 21 st Century” ( ART ‐21) Research Coordination Network has been to convene a series of workshops with community members to explore issues concerning research and training in plant biology, including the role that research using Arabidopsis thaliana can play in addressing those issues. A first workshop focused on training needs for bioinformatic and computational approaches in plant biology was held in 2016, and recommendations from that workshop have been published (Friesner et al., Plant Physiology , 175, 2017, 1499). In this white paper, we provide a summary of the discussions and insights arising from the second ART ‐21 workshop. The second workshop focused on experimental aspects of omics data acquisition and analysis and involved a broad spectrum of participants from academics and industry, ranging from graduate students through post‐doctorates, early career and established investigators. Our hope is that this article will inspire beginning and established scientists, corporations, and funding agencies to pursue directions in research and training identified by this workshop, capitalizing on the reference species Arabidopsis thaliana and other valuable plant systems.

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Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Cited by 129 publications

References 88 publications

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Orthodontics in the era of big data analytics

Directions for research and training in plant omics: Big Questions and Big Data

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