Deep Learning with Microfluidics for Biotechnology

Riordon, Jason; Sovilj, Dušan; Sanner, Scott; Sinton, David; Young, Elton T.

doi:10.1016/j.tibtech.2018.08.005

Cited by 178 publications

(129 citation statements)

References 93 publications

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“…DL depends on algorithms for reasoning process simulation and data mining, or for developing abstractions [17]. Hidden deep layers on DL maps input data to labels to analyze hidden patterns in complicated data [18]. Besides their use in medical X-ray recognition, DL architectures are also used in other areas in the application of image processing and computer vision in medical.…”

Section: Introductionmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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The coronavirus pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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Section: Introductionmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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“…We envision that microfluidics will continue to be integrated into different research topics and applications, with new innovation through combination with emerging disciplines, such as artificial intelligence,7,8 metamaterials9 and neuromorphic engineering 10. As the field has matured, microfluidics has played a key role in numerous applications and products that will reshape segmented markets.…”

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confidence: 99%

The Fourth Decade of Microfluidics

Kong

Shum

Weitz

2020

Small

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“…Multiple screening and selection technologies are also available for isolating high‐performance mutants from genetically diverse populations. Emerging microfluidic technologies will also drive a transition to automation, parallelization, and miniaturization of many screening assays . An often overlooked aspect of the whole process is the scaling‐up of fermentations using engineered MCFs, including critical performance indicators such as catalytic durability, genetic stability, and evolvability .…”

Section: Conclusion and The Way Aheadmentioning

confidence: 99%

“…Emerging microfluidic technologies will also drive a transition to automation, parallelization, and miniaturization of many screening assays. [99] An often overlooked aspect of the whole process is the scaling-up of fermentations using engineered MCFs, including critical performance indicators such as catalytic durability, genetic stability, and evolvability. [100] Although most of the (narrow) literature available on these matters deals with planktonic bacteria growing in liquid cultures, the same questions are likewise relevant for alternative cultivation setups, such as catalytic biofilms.…”

Section: Conclusion and The Way Aheadmentioning

confidence: 99%

Evolutionary Approaches for Engineering Industrially Relevant Phenotypes in Bacterial Cell Factories

Fernández-Cabezón

Cros

Nikel

2019

Biotechnology Journal

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The bio‐based production of added‐value compounds (with applications as pharmaceuticals, biofuels, food ingredients, and building blocks) using bacterial platforms is a well‐established industrial activity. The design and construction of microbial cell factories (MCFs) with robust and stable industrially relevant phenotypes, however, remains one of the biggest challenges of contemporary biotechnology. In this review, traditional and cutting‐edge approaches for optimizing the performance of MCFs for industrial bioprocesses, rooted on the engineering principle of natural evolution (i.e., genetic variation and selection), are discussed. State‐of‐the‐art techniques to manipulate and increase genetic variation in bacterial populations and to construct combinatorial libraries of strains, both globally (i.e., genome level) and locally (i.e., individual genes or pathways, and entire sections and gene clusters of the bacterial genome) are presented. Cutting‐edge screening and selection technologies applied to isolate MCFs displaying enhanced phenotypes are likewise discussed. The review article is closed by presenting future trends in the design and construction of a new generation of MCFs that will contribute to the long‐sought‐after transformation from a petrochemical industry to a veritable sustainable bio‐based industry.

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Deep Learning with Microfluidics for Biotechnology

Cited by 178 publications

References 93 publications

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

The Fourth Decade of Microfluidics

Evolutionary Approaches for Engineering Industrially Relevant Phenotypes in Bacterial Cell Factories

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