A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules – Proof of concept study using an artificial neural network for sample classification

Streun, Gabriel L; Elmiger, Marco P; Dobay, Akos; Ebert, Lars C.; Kræmer, Thomas

doi:10.1002/dta.2775

Cited by 22 publications

(15 citation statements)

References 22 publications

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“…A definitive distinction between a shallow and deep neural network is hard to define but around five to ten layers is the grey area between them, although with neural networks being produced that are hundreds of layers deep, the distinction may change in time. There are a range of software available to build neural networks such as R [73,74] , Matlab [75] and Python [6] . The most common method of building neural networks is in Python, using libraries such as PyTourch [73,74] and TensorFlow/Keras [6,9] .…”

Section: Neural Networkmentioning

confidence: 99%

“…There are a range of software available to build neural networks such as R [73,74] , Matlab [75] and Python [6] . The most common method of building neural networks is in Python, using libraries such as PyTourch [73,74] and TensorFlow/Keras [6,9] . PyTorch was developed by Facebook and is one of the most popular package alongside Googles TensorFlow [77] .…”

Section: Neural Networkmentioning

confidence: 99%

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Applications of machine learning in spectroscopy

Ramírez

Greenop

Ashton

et al. 2020

Applied Spectroscopy Reviews

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The way to analyze data in spectroscopy has changed substantially. At the same time, data science has evolved to the point where spectroscopy can find space to be housed, adapted and be functional. The integration of the two sciences has introduced a knowledge gap between data scientists who know about advanced machine learning techniques and spectroscopists who have a solid background in chemometrics. To reach a symbiosis, the knowledge gap requires bridging. This review article focuses on introducing data science subjects to non-specialist spectroscopists, or those unfamiliar with the subject. The article will explain concepts that are covered in machine learning, such as supervised learning, unsupervised learning, deep learning, and most importantly, the difference between machine learning and artificial intelligence. This article also includes examples of published spectroscopy research, in which some of the concepts explained here are applied. Machine learning together with spectroscopy can provide a useful, fast, and efficient tool to analyze samples of interest both for industrial and research purposes.

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Section: Neural Networkmentioning

confidence: 99%

Section: Neural Networkmentioning

confidence: 99%

Applications of machine learning in spectroscopy

Ramírez

Greenop

Ashton

et al. 2020

Applied Spectroscopy Reviews

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“…[ 23 ] Artificial neural network (ANN) is biologically system with an adaptive, self-learning, and computational construction simulating the functions of human neurons. [ 24 25 ] This technique can be trained to recognize and categorize complex patterns of diseases through an iterative learning process. Once proper training is achieved, the ANNs try to forecast with greater accuracy than traditional statistical techniques.…”

Section: Introductionmentioning

confidence: 99%

Developing an artificial neural network for detecting COVID-19 disease

Shanbehzadeh

Nopour

Kazemi-Arpanahi

2022

Journal of Education and Health Promotion

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BACKGROUND: From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19. MATERIALS AND METHODS: Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at P < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated. RESULTS: After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was introduced as the best configuration model for COVID-19 diagnosis. CONCLUSION: The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19.

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“…Arti cial Neural Networks (ANNs) is a supervised ML technique that imitates the tasks of biological human neurons based on a collection of connected nodes (input-hidden-output) called arti cial neurons (22,23). This method can be trained to recognize and categorize complex patterns of diseases and related healthcare events through an iterative learning process.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Orooji

Shanbehzadeh

Kazemi-Arpanahi³

et al. 2021

Preprint

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BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. ConclusionThe developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.

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A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules – Proof of concept study using an artificial neural network for sample classification

Cited by 22 publications

References 22 publications

Applications of machine learning in spectroscopy

Applications of machine learning in spectroscopy

Developing an artificial neural network for detecting COVID-19 disease

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

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