"GW-SPO Deep LSTM for Cancer Survival Prediction Using Gene-Expression Data"

What is it about?

Oncology treatment accuracy relies on providing information from a variety of sources to have a accurate assessment of a patient's health status and prediction. With the advancement in medical field, accurate prediction allows prescription of more effective treatments and customized medical services to individual patient's. Next generation sequencing has put pressure on cancer researchers in recent years by giving doctors access to vast amounts of data from RNA-seq high-throughput fields. Effectual survival prediction can save patient's life from threatening at earlier stage. In addition, traditional techniques of gene expression datasets failed to trade off balance among huge genes and low number of samples available, thereby resulting low level of survival prediction rate. Therefore, this research proposes an efficient model for survival prediction of cancer patients using proposed gray wolf-student psychology optimization-based deep long short term memory (GW-SPO based deep LSTM). The proposed GW-SPO is derived by incorporating gray wolf optimization (GWO) and student psychology based optimization (SPBO). However, survival prediction is performed effectively using deep LSTM and network classifier is trained using proposed GW-SPO. Nevertheless, proposed GW-SPO has achieved superior results with minimum RMSE of 0.325, and minimum prediction error of 0.110 for analysis with cluster size of 5.

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Why is it important?

The research underscores the growing importance of personalized medicine in oncology, where treatments and medical services are tailored to individual patients. This approach is made possible by accurate health status assessments and predictions, ultimately leading to more effective treatment prescriptions. Next generation sequencing (NGS) technologies, such as RNA-seq, provide vast amounts of data that can significantly enhance the understanding of a patient's cancer at the molecular level. This vast data resource puts pressure on researchers to extract meaningful insights for better diagnostic and treatment strategies.

Perspectives

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The adoption of next-generation sequencing technologies, such as RNA-seq, has revolutionized cancer research by providing unprecedented access to vast datasets. This wealth of information assists in developing more accurate assessments of a patient's health status, facilitating the prescription of effective treatments tailored to individual needs. Traditional methods of analyzing gene expression datasets often struggle with the imbalance between the large number of genes and the relatively small sample sizes available. This imbalance has historically led to lower rates of survival prediction accuracy, highlighting the need for more sophisticated models.

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