Chronological horse herd optimization-based gene selection

What is it about?

Cancer has always been one of the major hazards to human life which is also the most difficult part of human disease history. The death rate due to cancer is high. The prediction results are affected because of the major dissimilarities present in clinical results. Hence, it is necessary to enhance the accuracy of cancer survival prediction, which remains a challenging one. To defeat the challenges, this research devises a robust approach, named Deep Recurrent Neural Network-based Chronological Horse Herd Political Optimization (DRNN-based CHHPO) for survival prediction. Here, the gene selection is performed using the proposed Chronological Horse Optimization (CHO) by assuming the parameters of fitness, for example Minkowski distance plus Renyi entropy. The Horse Herd Optimization (HOA) and Chronological concept is merged to form the CHO. With the selected genes, the gene features are strengthened using technical indicators to enhance the overall process. Finally, survival prediction is completed by means of DRNN, which is trained by the CHHPO, which is the amalgamation of Political optimizer (PO) and CHO. Superior presentation with the Prediction Error (PE) and minimal Root Mean Square Error (RMSE) of 0.456 and 0.467 is accomplished by this developed technique

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Why is it important?

The appraisal of the gene-expression data is gaining rising attention in the precision medicine field in recent times. It creates a structure for the growth and investigation in a series of clinical events intended at producing medicine more participatory, personalized, preventive and predictive. An unparalleled amount of data has been produced from which disciplines such as proteomics, epigenetics, genomics, transcriptomics, or metabolomics are favored [1]. As cancer is a heterogeneous disease obsessed with diverse genomic variations [2], the assessment of gene-expression data generated from tumor tissue samples permits the revision of molecular factors contributing to disease progression over time or influencing a patient’s survival.

Perspectives

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This paper introduces a novel survival prediction method using DRNN-based CHHPO for cancer survival prediction. Here, the box-cox transformation method is used for data transformation, and thereafter the process of gene selection is done using the CHO by assuming the objectives of fitness. The CHO is premeditated by the integration of the Chronological idea and CHO. Moreover, the features of selected genes are strengthened using technical indicators for achieving effective prediction outcomes.

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