Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study

Alsenan, Shrooq; Al-Turaiki, Isra; Hafez, Alaaeldin M.

doi:10.1109/access.2020.2990375

Cited by 32 publications

(13 citation statements)

References 73 publications

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“…SMOTE has been applied in a variety of applications with demonstrated success. 35 The technique has also been shown to be robust and to perform better than simple oversampling. SMOTE is also effective for the reduction of overfitting.…”

Section: Methodsmentioning

confidence: 99%

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Al-Turaiki

Altwaijry

2021

Big Data

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Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network. In this article, we propose two models based on deep learning to address the binary and multiclass classification of network attacks. We use a convolutional neural network architecture for our models. In addition, a hybrid two-step preprocessing approach is proposed to generate meaningful features. The proposed approach combines dimensionality reduction and feature engineering using deep feature synthesis. The performance of our models is evaluated using two benchmark data sets, namely the network security laboratory-knowledge discovery in databases data set and the University of New South Wales Network Based 2015 data set. The performance is compared with similar deep learning approaches in the literature, as well as state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall, outperforming similar models in the literature.

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Section: Methodsmentioning

confidence: 99%

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Al-Turaiki

Altwaijry

2021

Big Data

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“…One important attempt is feature engineering from small molecule structures using deep neural networks. Spurred by the increasing number of large molecular databases that are available for pharmaceutical research, several DL methods have recently been proposed to obtain features from 2D molecular information 68 . Drugs, as all small molecules, are composed of atoms and chemical bonds.…”

Section: Encoding Small Molecule Structures To Improve DL Frameworkmentioning

confidence: 99%

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)‐based small molecule structure elucidation

Liu

Vijlder

Bittremieux

et al. 2021

Rapid Comm Mass Spectrometry

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RATIONALE:Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from MS/MS data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation. ARCHITECTURES:Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: 1) computational complexity because of the separate training of descriptor-specific classifiers, 2) the high dimensional nature of mass spectral data and information loss due data preprocessing, 3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For 1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For 2) and 3), we introduce feature engineering to extract condensed and higherorder information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search. CONCLUSIONS:We believe that in principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their This article is protected by copyright. All rights reserved.performance and general applicability should be fairly evaluated against classical machine learning frameworks.

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“…There are many proprietary and free software packages for calculating MD [31][32][33]. Types of MD and their usage, in particular for RI prediction, were extensively reviewed in previous works [24,[33][34][35][36]. A typical diverse set of MD contains features that are very diverse in nature: integer and real numbers, categorical features with different meanings.…”

Section: Introductionmentioning

confidence: 99%

Gas Chromatographic Retention Index Prediction Using Multimodal Machine Learning

Matyushin

Buryak

2020

IEEE Access

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Gas chromatography is a widely used method in analytical chemistry and metabolomics. Using gas chromatography, vaporizable compounds can be separated for their further identification. Retention indices are standardized values that depend only on a chemical structure of a compound and on a stationary phase and characterize the retention of a compound in a chromatographic system. Retention index prediction is an important task because databases contain experimental values for a small fraction of all possible molecules, while this information is usable for untargeted analysis. In this work, we consider four machine learning models for retention index prediction: 1D and 2D convolutional neural networks, deep residual multilayer perceptron, and gradient boosting. String representation of the molecule, 2D representation of the chemical structure, molecular descriptors and fingerprints, and molecular descriptors are used as inputs of these four models, respectively, along with information about the stationary phase. The first and third models show the best performance, while the other two perform slightly worse. The models predict retention index values for various standard and semi-standard non-polar stationary phases. Further improvement in performance was achieved using a linear model that uses the results of four previous models as inputs (model stacking). The models were tested using various diverse data sets: flavor compounds, essential oils, metabolomics-related compounds. Achieved accuracy: median absolute and percentage errors -6-40 units and 0.8-2.2%. Accuracy depends on a test data set. The stacking model outperforms previously reported approaches for all test data sets. Parameters of a pre-trained model and some source code are provided.INDEX TERMS Analytical chemistry, convolutional neural network, deep learning, gas chromatography, gradient boosting, residual neural network, retention index, untargeted chemical analysis.

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Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study

Cited by 32 publications

References 73 publications

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)‐based small molecule structure elucidation

Gas Chromatographic Retention Index Prediction Using Multimodal Machine Learning

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