Advertisement

Topics

Systems Metabolic Engineering Meets Machine Learning: A new era for data-driven metabolic engineering.

08:00 EDT 30th March 2019 | BioPortfolio

Summary of "Systems Metabolic Engineering Meets Machine Learning: A new era for data-driven metabolic engineering."

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review we discuss the nature of 'omics and provide a broad introduction to the machine learning algorithms combining these datasets into predictive models of metabolism and metabolic rewiring. Next, we highlight recent work in the literature that utilize such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using machine learning algorithms with metabolic engineering and systems biology related datasets. This article is protected by copyright. All rights reserved.

Affiliation

Journal Details

This article was published in the following journal.

Name: Biotechnology journal
ISSN: 1860-7314
Pages: e1800416

Links

DeepDyve research library

PubMed Articles [26655 Associated PubMed Articles listed on BioPortfolio]

The Roles of Supervised Machine Learning in Systems Neuroscience.

Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of syst...

The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data.

Recent years have seen a surge of studies in machine learning in health and biomedicine, driven by digitalization of healthcare environments and increasingly accessible computer systems for conducting...

Machine learning in human olfactory research.

The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data driven approaches that complement hypothesis driven resear...

Structural biology meets data science: does anything change?

Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by...

Machine learning and big data in psychiatry: toward clinical applications.

Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cul...

Clinical Trials [7870 Associated Clinical Trials listed on BioPortfolio]

Optimized Multi-modality Machine Learning Approach During Cardio-toxic Chemotherapy to Predict Arising Heart Failure

The present project will develop an automated machine learning approach using multi-modality data (imaging, laboratory, electrocardiography and questionnaire) to increase the understanding...

Machine Learning for Handheld Vascular Studies

The use of handheld arterial 'stethoscopes' (continuous wave Doppler devices) are ubiquitous in clinical practice. However, most users have received no formal training in their use or the ...

Assessment of Utility of accelerateIQ in the Care of Patients Participating in a Pulmonary Rehabilitation Program

The proposed study seeks to assess the performance of continuous biosensor data and machine learning analytics in assessment of health patient status in a pulmonary rehabilitation program....

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascu...

Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this ris...

Medical and Biotech [MESH] Definitions

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

Methods and techniques used to modify or select cells and develop conditions for growing cells for biosynthetic production of molecules (METABOLIC ENGINEERING), for generation of tissue structures and organs in vitro (TISSUE ENGINEERING), or for other BIOENGINEERING research objectives.

Advertisement
Quick Search
Advertisement
Advertisement

 


DeepDyve research library

Relevant Topic

Bioscience
Bioscience - any of the sciences that deal with living organisms.  The study of the nature, behavior, and uses of living organisms as applied to biology. Any of the branches of natural science dealing with living things, such as their structure, b...


Searches Linking to this Article