Rider-chicken optimization dependent recurrent neural network for cancer detection and classification using gene expression data

Aher, Chetan Nimba; Jena, Ajay Kumar

doi:10.1080/21681163.2020.1830436

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…They combined a Long short-term memory (LSTM) network with the Artificial Immune Recognition System (AIRS) and achieved 89.6% accuracy. RCO-RNN was introduced by Aher et al [104] and employed the Rider Chicken Optimization (RCO) method to extract relevant genes in gene expression data, which were afterward categorized with an RNN. On the Leukemia database, Small Blue Round Cell Tumor (SBRCT) dataset, and Lung Cancer Dataset, RCO-RNN achieved a 95% accuracy rate.…”

Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

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Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

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Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

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“…RNN is suitable for dealing with timing-related issues such as video, voice, and text. In the field of medical images, an RNN is used to assist in cancer classification and assessment [174][175][176][177], prediction [178,179], and clinical data modeling [180]. In order to achieve better accuracy, RNNs are often combined with other networks (such as convolutional cyclic neural network [177,181], convolutional grid neural network [182], hybrid method of the recurrent neural network and graph neural network (RGNN) [183], and wavelet recurrent neural network [184]), or the internal structure of RNNs can be improved (such as bidirectional recurrent neural network [178,185], long short-term memory network (LSTM) [186], and fuzzy recurrent neural network (FR-Net) [187]).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

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“…The studies [23][24][25] delve into the meticulous application of recurrent neural networks (RNNs) for diverse approaches to understanding and classifying gene regulatory networks (GRNs) and cancer detection from gene expression data. One approach [23] leverages a dual-attention RNN to not only predict gene temporal dynamics with high accuracy across various GRN architectures but also exploit the attention mechanism of RNNs, employing graph theory tools to hierarchically distinguish different architectures of the GRN, though the robustness of this method against varied noise types and its applicability to non-synthetic data present potential limitations.…”

Section: Related Workmentioning

confidence: 99%

“…Another research study [24] introduces a strategy for cancer classification using a JayaAnt lion optimization-based Deep RNN (JayaALO-based DeepRNN), involving data normalization, transformation, feature dimension detection, and classification, and while achieving high classification accuracy, sensitivity, and specificity, the method's generalized applicability and performance consistency across different types and stages of cancers are yet to be fully explored. In a similar vein, the third study [25] proposes a Rider Chicken Optimization algorithm-dependent RNN (RCO-RNN) classifier for cancer detection and classification, which, despite demonstrating promising results across several datasets, still demands a thorough investigation regarding its performance on varied genomic profiles and under possible computational constraints.…”

Section: Related Workmentioning

confidence: 99%

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

Babichev,

Liakh,

Kalinina

2023

Applied Sciences

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The importance of gene expression data processing in solving the classification task is determined by its ability to discern intricate patterns and relationships within genetic information, enabling the precise categorization and understanding of various gene expression profiles and their consequential impacts on biological processes and traits. In this study, we investigated various architectures and types of recurrent neural networks focusing on gene expression data. The effectiveness of the appropriate model was evaluated using various classification quality criteria based on type 1 and type 2 errors. Moreover, we calculated the integrated F1-score index using the Harrington desirability method, the value of which allowed us to improve the objectivity of the decision making when model effectiveness was evaluated. The final decision regarding model effectiveness was made based on a comprehensive classification quality criterion, which was calculated as the weighted sum of classification accuracy, integrated F1-score index, and loss function values. The simulation results show higher appeal of a single-layer GRU recurrent network with 75 neurons in the recurrent layer. We also compared convolutional and recurrent neural networks on gene expression data classification. Although convolutional neural networks showcase benefits in terms of loss function value and training time, a comparative analysis revealed that in terms of classification accuracy calculated on the test data subset, the GRU neural network model is slightly better than the CNN and LSTM models. The classification accuracy when using the GRU network was 97.2%; in other cases, it was 97.1%. In the first case, 954 out of 981 objects were correctly identified. In other cases, 952 objects were correctly identified.

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Rider-chicken optimization dependent recurrent neural network for cancer detection and classification using gene expression data

Cited by 16 publications

References 27 publications

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Deep Learning for Medical Image-Based Cancer Diagnosis

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

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