DeepReI: Deep learning-based gas chromatographic retention index predictor

Vrzal, Tomáš; Malečková, Michaela; Olšovská, Jana

doi:10.1016/j.aca.2020.12.043

Cited by 29 publications

(27 citation statements)

References 23 publications

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“…For classification-oriented deep neural networks, a softmax layer and a classification layer must follow the last FCL. Also, batch normalization (Vrzal et al, 2021) layers are embedded as assisting layers.…”

Section: Human Visual System and Attention Mechanismmentioning

confidence: 99%

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Wang

Zhou

Yang

et al. 2021

Front. Aging Neurosci.

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Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately.Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance.Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852.Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation.

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Section: Human Visual System and Attention Mechanismmentioning

confidence: 99%

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

Wang

Zhou

Yang

et al. 2021

Front. Aging Neurosci.

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“…The l 2 -norm pooling (L2P), average pooling (AP) [14], and max pooling (MP) [15] produce the l 2 -norm, average, and maximum values within the block B m 1 ,m 2 , respectively. Their formula can be written as below:…”

Section: Stochastic Poolingmentioning

confidence: 99%

“…In contrast, MP stores the greatest value but deteriorates the overfitting obstacle. In order to solve the above concerns, stochastic pooling (SP) [15] is introduced to provide a resolution to the drawbacks of AP and MP. SP is a four-step process.…”

Section: Stochastic Poolingmentioning

confidence: 99%

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SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition

Wang

Chu³

et al. 2021

Wireless Communications and Mobile Computing

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Aim. This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). Methods. Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. Results. The results on ten runs of 10-fold crossvalidation show that our SOSPCNN model yields a sensitivity of 92.25 ± 2.19 , a specificity of 92.75 ± 2.49 , a precision of 92.79 ± 2.29 , an accuracy of 92.50 ± 1.18 , an F1 score of 92.48 ± 1.17 , an MCC of 85.06 ± 2.38 , an FMI of 92.50 ± 1.17 , and an AUC of 0.9587. Conclusion. The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.

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“…An important task is the development of versatile RI prediction models that are applicable to almost arbitrary structures. There are several works (e.g., the most recent works [17][18][19]) that are devoted to RI prediction for diverse compounds and use data sets ranging in size from hundreds to tens of thousands of compounds. Most of such works, except for the most recent ones, are extensively reviewed in our previous work [17].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Matyushin

Sholokhova

Buryak

2021

IJMS

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Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

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DeepReI: Deep learning-based gas chromatographic retention index predictor

Cited by 29 publications

References 23 publications

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

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