EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction

Mahdaddi, Abla; Meshoul, Souham; Belguidoum, Meriem

doi:10.1016/j.eswa.2021.115525

Cited by 32 publications

(11 citation statements)

References 79 publications

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“…We also selected the popular deep learning methods for comparison, including CNN [13] , DeepLSTM, CNN-AbiLSTM [14] , MPNN [15] , and transformer. With the same data set, we conducted pairwise experiments.…”

Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

Drug-target interaction prediction using high order nonlinear features via neural factorization machines

Zhu

2022

J. Phys.: Conf. Ser.

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Medication design and repositioning are sped up by the prediction of drug-target interactions (DTIs). Two main kinds of prediction methods are commonly used, which are based on chemical structure feature extraction and deep learning methods. However, on the one hand, the DTI prediction approaches based on chemical structural feature extraction may not wholly explore the possible network characteristics in the data. On the other hand, many deep learning methods call for numerous layers of neural networks to be layered to learn higher-order feature interaction information. To sum up, the existing computation methods often have the limitations of gradient disappearance and overfitting. This study presents a novel method (JRD-NFM) by calculating Jaccard similarities, getting an eigenvector through Restarted random walk (RWR), and generating low-dimensional feature vectors by Disposition Component Analysis (DCA). Besides decoding the topological features and similarity information of target and drug node, it can also get the context information of a single network. Considering the advantages of Neural Factorization Machines (NFM) in extracting high-order nonlinear features and processing sparse data, this study use NFM to classifier the collection data to integrate drug and target biochemical structure information. The experimental results demonstrate that JRD-NFM can outperform widely used deep learning methods and conventional chemical structure approaches. It could provide fresh ideas for combining molecular structure and heterogeneous network data to predict DTIs.

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Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

Drug-target interaction prediction using high order nonlinear features via neural factorization machines

Zhu

2022

J. Phys.: Conf. Ser.

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“…In addition to its comprehensivess as a searching algorithm, grid search aims to identify the most optimal values for hyperparameters through a manually specified subset of hyperparameter space [ 36 ]. However, since the grid of configurations grows exponentially depending on the number of hyperparameters during the hyperparameter optimization process, the algorithm is not often useful for the optimization of deep neural networks [ 36 ]. During the hyperparameter optimization in CNN, it may take a few hours or a whole day to evaluate a hyperparameter selection, which causes serious computational problems.…”

Section: Related Workmentioning

confidence: 99%

“…One of the main advantages in Bayesian optimization–based neural network optimization is that it does not require running neural network completely. On the other hand, its complexity and high-dimensional hyperparameter space makes Bayesian optimization an impractical and expensive approach for hyperparameter optimization [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

Kiziloluk

Sert

2022

Med Biol Eng Comput

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Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription–polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people’s lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch. Graphical abstract

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“…Thus, the proposed model outperformed these other advanced models in terms of error minimization, thereby improving wind energy forecasting accuracy. Additionally, Mahdaddi et al [22] presented an evolutionary Int J Elec & Comp Eng ISSN: 2088-8708  method (EA) framework, namely the differential evolution (DE) algorithm, for determining the ideal configuration for the proposed model's hyper-parameters. The suggested model, CNN-attention-based bidirectional long short-term memory network (CNN-AbiLSTM), was compared using a manually built deep learning model and an EA-based hyper-parameter optimization technique.…”

Section: Introductionmentioning

confidence: 99%

Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

Ann

Pebrianti

Abas

et al. 2023

IJECE

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<p>Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization.</p>

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EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction

Cited by 32 publications

References 79 publications

Drug-target interaction prediction using high order nonlinear features via neural factorization machines

Drug-target interaction prediction using high order nonlinear features via neural factorization machines

COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

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