RETRACTED ARTICLE: Recurrent Neural Network for Genome Sequencing for Personalized Cancer Treatment in Precision Healthcare

Suresh, A.; Nair, Resmi R.; Neeba, E. A.; Kumar, Sathish

doi:10.1007/s11063-021-10572-0

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…The system intends to aid the public health sector in arranging essential resources in advance and minimizing the fatality rate by utilizing Google heatmaps to indicate the areas where dengue is expected to occur. The authors in reference 89 suggested an optimized LSTM method, to interpret genome sequencing for personalized cancer treatment. Its primary focus is to detect new disease‐related variants and genes, and it has shown superior accuracy performance than conventional hybrid classifiers.…”

Section: Approaches For Personalized Health Monitoringmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

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Section: Approaches For Personalized Health Monitoringmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Majji et al [92]. put forth the JayaALO-based DeepRNN, a cutting-edge technique for automated cancer prediction that melded the Jaya ant lion optimization (ALO) with an RNN structure [93]. Their method exhibited peak accuracy, reaching 95.97% across varied datasets such as AP Colon Kidney, AP Breast Ovary, and others.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Utilizing Machine Learning Techniques for Categorizing Cancer Based on Gene Expression Data: A Review

Hembrom T., Hazra S., Barman D.

2024

jes

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Cancer is a group of diseases which share one common feature: the growth of abnormal cells, thus ranking as the second leading cause of death globally, after cardiovascular diseases in WHO's report. Evaluating gene expression is based on the fact that it is the genesis of early cancer detection, the concurrence of the molecular and genetic processes. By using DNA microarray techniques and RNA-sequencing approaches, researchers in computational genomics can give quantitative measures of gene expression levels providing very accurate input data for computational evaluation. The current paper is about machine learning technology, which identifies cancer subtypes according to patterns of gene expression. It embraces the two distinct methodologies which are traditional plus deep learning with high proficiency focused on the cancer-related gene. The outline includes the most popular deep neural network designs such as MLPs, CNN, RNN, GNN, and the recently emerged Transformer networks. The review describes common data collection methods used in this field and some of the essential datasets for supervised machine learning. In addition, the specific techniques developed to cover the complicated horizontal spread of gene expression data are also presented. The article explores theoretical possibilities for the promotion of machine learning-based gene expression analysis in cancer classification towards the end.

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“…The system intends to aid the public health sector in arranging essential resources in advance and minimizing the fatality rate by utilizing Google heatmaps to indicate the areas where dengue is expected to occur. The authors in [82] suggested an optimized LSTM method, to interpret genome sequencing for personalized cancer treatment. Its primary focus is to detect new disease-related variants and genes, and it has shown superior accuracy performance than conventional hybrid classifiers.…”

Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

Deep Learning for Personalized Health Monitoring and Prediction: A Review

Damaševicˇius,

Jagatheesaperumal

et al. 2023

Preprint

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Personalized health monitoring and prediction have become essential for improving health- care delivery, especially with the growing prevalence of chronic diseases and an aging population. Deep learning (DL) has emerged as a promising approach for developing personalized health mon- itoring systems that can predict health outcomes accurately and efficiently. With the increasing availability of personal health data, DL-based methods have emerged as a promising approach to improve healthcare delivery by providing accurate and timely predictions of health outcomes. This article provides a comprehensive review of the recent developments in the application of DL for personalized health monitoring and prediction. It summarizes various DL architectures and their applications for personalized health monitoring, including wearable devices, electronic health records, and social media data. Furthermore, the article also explores the challenges and future directions for the application of DL in personalized health monitoring. valuable insights into the potential of DL for personalized health monitoring and prediction.

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RETRACTED ARTICLE: Recurrent Neural Network for Genome Sequencing for Personalized Cancer Treatment in Precision Healthcare

Cited by 13 publications

References 21 publications

Deep learning for personalized health monitoring and prediction: A review

Deep learning for personalized health monitoring and prediction: A review

Utilizing Machine Learning Techniques for Categorizing Cancer Based on Gene Expression Data: A Review

Deep Learning for Personalized Health Monitoring and Prediction: A Review

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