Application of Machine Learning in Microbiology

Qu, Kaiyang; Guo, Fei; Liu, Xiangrong; Lin, Yandan; Zou, Quan

doi:10.3389/fmicb.2019.00827

Cited by 160 publications

(85 citation statements)

References 130 publications

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“…These predictions are subsequently averaged to obtain an estimation of the error rate of the OOB. The generalization error of the RF model depends on the weight of the individual trees and the correlations between them (Qu et al, 2019). The building of a RT follows a recursive binary partition approach starting at the root node and divides the non-correlated variables into two new branches.…”

Section: Overview Of the Random Forest (Rf) Methodsmentioning

confidence: 99%

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

et al. 2020

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Real-time monitoring of soil matric potential has now become a common practice for precision irrigation management. Some crops, such as cranberries, are susceptible to both water and anoxic stresses. Excessive variations in soil matric potential in the root zone may reduce plant transpiration, due to either saturated or dry soil conditions, thereby reducing productivity. A timely supply of the right amount of water is, therefore, fundamental for efficient irrigation management. In this paper, we compare the capabilities of a machine learning-based model and a physics-based model to predict soil matric potential in the root zone. The machine learning model is a random forest algorithm, while the physics-based model is a two-dimensional solver of Richards equation (HYDRUS 2D). After training and calibration on a dataset collected in a cranberry field located in Québec (Canada), the performance of the two models is evaluated for 30 different time frames of 72-h soil matric potential forecasts. The results highlight that both models can accurately forecast the soil matric potential in the root zone. The machine learning-based model can achieve better performance when compared to the physics-based model, but forecasting accuracy decreases rapidly toward the end of the 72-h lead time, while the error for the Richards equation-based model does not increase with time and remain small compared to the typical measurement error.

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Section: Overview Of the Random Forest (Rf) Methodsmentioning

confidence: 99%

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

et al. 2020

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“…We provide readers insight into important methods, challenges that arise, suggested solutions as well as blueprints of example scenarios to be used in their research. See Qu et al (2019), Topçuoglu et al (2019), and Zhou and Gallins (2019) for further explanation and examples of the machine learning methods discussed here.…”

Section: Computational Analysismentioning

confidence: 99%

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health

et al. 2020

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Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge.

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“…Moreover, automated image segmentation and pattern recognition have been emphasized in image analysis due to the need for processing a large number of images, which is a laborious task. Recently, advances in machine learning and deep learning algorithms, and the increasing availability of large labeled/annotated datasets (such as ImageNet, AlexNet, ResNet, Inception‐V4 databases) and pre‐trained models, are leading to remarkable improvements in the automated analysis of images, attracting especially the attention of the scientific community mostly in clinical studies (Miotto, Wang, Wang, Jiang & Dudley, ; Caixinha & Nunes, ; Yang, Xie, Liu, Cao, & Guo, ), in remote sensing (Ma et al, ; Tsagkatakis, Aidini, Fotiadou, & Giannopoulos, ) and in microbiology (Qu, Guo, Liu, Lin, & Zou, ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning based automated analysis of archaeo‐geophysical images

Küçükdemirci

Sarris

2020

Archaeological Prospection

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Thanks to recent advances in deep learning (DL) and the increasing availability of large labeled/annotated datasets and trained network models, there has been impressive progress in the automated analysis of images from different scientific domains such as medicine, microbiology, astronomy and remote sensing. The automated analysis of archaeo-geophysical data is also considered important due to the large spatial extent of areas covered by landscape surveys using multi-sensor arrays driven by motorized carts and subsequently the large volume of collected data. In this work, a convolutional neural network (CNN) is built by Python 3.6 programming language using the Deep Learning Library of Keras with Tensorflow backends, a library that implements the building blocks for CNN. The network is trained from scratch adopting U-Net architecture to accomplish an automatic analysis of the archaeo-geophysical features with emphasis on ground-penetrating radar (GPR) anomalies. K E Y W O R D S archaeo-geophysics, convolutional neural networks (CNNs), deep learning, feature extraction, GPR (ground-penetrating radar), U-Net

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Application of Machine Learning in Microbiology

Cited by 160 publications

References 130 publications

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health

Deep learning based automated analysis of archaeo‐geophysical images

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