scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools

2021 ◽  
Vol 11 (19) ◽  
pp. 8861
Author(s):  
Philipp Ruf ◽  
Manav Madan ◽  
Christoph Reich ◽  
Djaffar Ould-Abdeslam
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Open Source ◽  
Model Performance ◽  
Network Models ◽  
System Quality ◽  
Neural Network Models ◽  
Automated Machine Learning ◽  
Learning Projects ◽  
Selection Of

Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project development management arises. Due to the multitude of tools available for different operational phases, responsibilities and requirements become more and more unclear. In this work, Machine Learning Operations (MLOps) technologies and tools for every part of the overall project pipeline, as well as involved roles, are examined and clearly defined. With the focus on the inter-connectivity of specific tools and comparison by well-selected requirements of MLOps, model performance, input data, and system quality metrics are briefly discussed. By identifying aspects of machine learning, which can be reused from project to project, open-source tools which help in specific parts of the pipeline, and possible combinations, an overview of support in MLOps is given. Deep learning has revolutionized the field of Image processing, and building an automated machine learning workflow for object detection is of great interest for many organizations. For this, a simple MLOps workflow for object detection with images is portrayed.

scholarly journals Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis

2021 ◽  
Author(s):  
Robert J. O’Shea ◽  
Amy Rose Sharkey ◽  
Gary J. R. Cook ◽  
Vicky Goh
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Model Performance ◽  
Network Models ◽  
Imaging Studies ◽  
Neural Network Models ◽  
Eligibility Criteria ◽  
Data Partitions

Abstract Objectives To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. Methods A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. Results One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21–34%), 31% reported demographics for their study population (58/186, 95% CI 25–39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42–57%). Median CLAIM compliance was 0.40 (IQR 0.33–0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). Conclusions Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis. Key Points • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.

scholarly journals Population Pharmacokinetics of Doxycycline in Children

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Elizabeth J. Thompson ◽  
Huali Wu ◽  
Chiara Melloni ◽  
Stephen Balevic ◽  
Janice E. Sullivan ◽  
...  
Keyword(s):  
Model Performance ◽  
Standard Of Care ◽  
Volume Of Distribution ◽  
Clinical Value ◽  
Final Model ◽  
Childhood Infections ◽  
Obesity Status ◽  
Improve Model ◽  
Using Data ◽  
Improve Model Performance

ABSTRACT Doxycycline is a tetracycline-class antimicrobial labeled by the U.S. Food and Drug Administration for children >8 years of age for many common childhood infections. Doxycycline is not labeled for children ≤8 years of age, due to the association between tetracycline-class antibiotics and tooth staining, although doxycycline may be used off-label under severe conditions. Accordingly, there is a paucity of pharmacokinetic (PK) data to guide dosing in children 8 years and younger. We leveraged opportunistically collected plasma samples after intravenous (i.v.) and oral doxycycline doses received per standard of care to characterize the PK of doxycycline in children of different ages and evaluated the effect of obesity and fasting status on PK parameters. We developed a population PK model of doxycycline using data collected from 47 patients 0 to 18 years of age, including 14 participants ≤8 years. We developed a 1-compartment PK model and found doxycycline clearance to be 3.32 liters/h/70 kg of body weight and volume to be 96.8 liters/70 kg for all patients, comparable to values reported in adults. We estimated a bioavailability of 89.6%, also consistent with adult data. Allometrically scaled clearance and volume of distribution did not differ between children 2 to ≤8 years of age and children >8 to ≤18 years of age, suggesting that younger children may be given the same per-kilogram dosing. Obesity status and fasting status were not selected for inclusion in the final model. Additional doxycycline PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

Artificial Metabolic Networks: enabling neural computation with metabolic networks

2022 ◽  
Author(s):  
Leon Faure ◽  
Bastien Mollet ◽  
Wolfram Liebermeister ◽  
Jean-Loup Faulon
Keyword(s):  
Machine Learning ◽  
Recurrent Neural Networks ◽  
Metabolic Networks ◽  
Cross Validation ◽  
Regression Coefficient ◽  
Network Models ◽  
Biotechnological Applications ◽  
Neural Network Models ◽  
Surrogate Constraint ◽  
Constraint Based Modeling

Metabolic networks have largely been exploited as mechanistic tools to predict the behavior of microorganisms with a defined genotype in different environments. However, flux predictions by constraint-based modeling approaches are limited in quality unless labor-intensive experiments including the measurement of media intake fluxes, are performed. Using machine learning instead of an optimization of biomass flux - on which most existing constraint-based methods are based - provides ways to improve flux and growth rate predictions. In this paper, we show how Recurrent Neural Networks can surrogate constraint-based modeling and make metabolic networks suitable for backpropagation and consequently be used as an architecture for machine learning. We refer to our hybrid - mechanistic and neural network - models as Artificial Metabolic Networks (AMN). We showcase AMN and illustrate its performance with an experimental dataset of Escherichia coli growth rates in 73 different media compositions. We reach a regression coefficient of R2=0.78 on cross-validation sets. We expect AMNs to provide easier discovery of metabolic insights and prompt new biotechnological applications.

scholarly journals Improving biomedical named entity recognition with syntactic information

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanhe Tian ◽  
Wang Shen ◽  
Yan Song ◽  
Fei Xia ◽  
Min He ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Model Performance ◽  
Entity Recognition ◽  
Input Word ◽  
Named Entity ◽  
Improve Model ◽  
Syntactic Information ◽  
Biomedical Texts ◽  
Improve Model Performance ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain. Previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to directly concatenating the embeddings of syntactic information to the input word embeddings. Therefore, such syntactic information is leveraged in an inflexible way, where inaccurate one may hurt model performance. Results In this paper, we propose BioKMNER, a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information. We evaluate BioKMNER on six English biomedical datasets, where our method with KVMN outperforms the strong baseline method, namely, BioBERT, from the previous study on all datasets. Specifically, the F1 scores of our best performing model are 85.29% on BC2GM, 77.83% on JNLPBA, 94.22% on BC5CDR-chemical, 90.08% on NCBI-disease, 89.24% on LINNAEUS, and 76.33% on Species-800, where state-of-the-art performance is obtained on four of them (i.e., BC2GM, BC5CDR-chemical, NCBI-disease, and Species-800). Conclusion The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

2021 ◽  
Vol 268 ◽  
pp. 115951
Author(s):  
Xiangyu Xu ◽  
Ning Qin ◽  
Zhenchun Yang ◽  
Yunwei Liu ◽  
Suzhen Cao ◽  
...  
Keyword(s):  
Model Performance ◽  
Mobile Monitoring ◽  
Short Term ◽  
Improve Model ◽  
Improve Model Performance

scholarly journals Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 689 ◽  
Author(s):  
Tyler McCandless ◽  
Susan Dettling ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Solar Power ◽  
Network Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Power Prediction ◽  
Neural Network Models

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

scholarly journals Probing Behavior of Diaphorina citri (Hemiptera: Liviidae) on Valencia Orange Influenced by Sex, Color, and Size

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Timothy A Ebert ◽  
Michael E Rogers
Keyword(s):  
Body Size ◽  
Model Performance ◽  
Diaphorina Citri ◽  
Probing Behavior ◽  
Valencia Orange ◽  
Improve Model ◽  
Candidatus Liberibacter ◽  
Citrus Huanglongbing ◽  
Liberibacter Asiaticus ◽  
Improve Model Performance

Abstract Candidatus Liberibacter asiaticus Jagoueix, Bové, and Garnier (Rhizobiales: Rhizobiaceae) is transmitted by the psyllid Diaphorina citri Kuwayama and putatively causes Huanglongbing disease in citrus. Huanglongbing has reduced yields by 68% relative to pre-disease yields in Florida. Disease management is partly through vector control. Understanding vector biology is essential in this endeavor. Our goal was to document differences in probing behavior linked to sex. Based on both a literature review and our results, we conclude that there is either no effect of sex or that identifying such an effect requires a sample size at least four times larger than standard methodologies. Including both color and sex in statistical models did not improve model performance. Both sex and color are correlated with body size, and body size has not been considered in previous studies on sex in D. citri in terms of probing behavior. An effect of body size was found wherein larger psyllids took longer to reach ingestion behaviors and larger individuals spent more time-ingesting phloem, but these relationships explained little of the variability in these data. We suggest that the effects of sex can be ignored when running EPG experiments on healthy psyllids.

scholarly journals Machine Learning in KM3NeT

2019 ◽  
Vol 207 ◽  
pp. 05004 ◽  
Author(s):  
Chiara De Sio
Keyword(s):  
Machine Learning ◽  
Network Models ◽  
High Energy ◽  
Particle Identification ◽  
Machine Learning Techniques ◽  
Neutrino Interaction ◽  
Mass Hierarchy ◽  
Reconstruction Algorithms ◽  
Neural Network Models ◽  
Neutrino Mass Hierarchy

The KM3NeT Collaboration is building a network of underwater Cherenkov telescopes at two sites in the Mediterranean Sea, with the main goals of investigating astrophysical sources of high-energy neutrinos (ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification, signal/background discrimination and data analysis, with sound results as well as promising research paths. The main focus of this work is the application of Convolutional Neural Network models to the tasks of neutrino interaction classification, as well as the estimation of energy and direction of the propagating particles. The performances are also compared to those of the standard reconstruction algorithms used in the Collaboration.

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