Integrating lncRNA gene signature and risk score topredict recurrence cervical cancer using recurrent neural network

Abstract

Cervical Cancer (CC) have significant ramification on women's lives worldwide. One-fifth of every woman incurring cervical cancer pertains to India. This research aims to identify cervical cancer patients who have undergone treatment and diagnosis for recurrence cervical cancer and educate them on further clinical treatmentfor recurrence cervical cancer (CC). The proposed work mainly constitutes the identification of lncRNA (Long Non-Coding RNA) for predicting recurrence cervical genes and undergoing natural medication by implementing the HSIC model with correlation matrix for identifying the recurrence cancer genes.The recurrent Neural Network (RNN) model is established to identify the hub genes relevant to the recurrence of CC. We propose to use a Long Short-Term Memory (LSTM) model to predict the spread of CC to a certain extent. The propounded model classifies the CC cells associated with the gene signatures with two stages. Recurrence CC patients can be identified with the Artificial Fish Swarm Algorithm (AFSA). This algorithm is deployed for recurrence CC feature selection based on the gene signature.The decision-making therapy is deployed after the post-recurrence CC.The proposed method is dealt with risk value with positive and negative score showing the high risk of recurrence cervical cancer and low risk of cervical cancer.The suggested algorithm shows 45.987 positive score and −32.654 negative risk score. The proposed outcome of the research is to identify the risk score associated with the gene signatures.