Classification of Hidden Patterns in Cancer Genome using Deep Learning

The complex structures in cancer genomes are obtained through the revolution of high-throughput technology. The major challenge involves the interpretation of complex sceneries of genomic alterations with the impact of diagnosis and prognosis. The proposed system includes deep-learning techniques that provide optimum solutions for evaluating hidden patterns of the cancer genome. They help to extract important information such as mutation and detection of therapeutic targets. The proposed system involves the aid of a Convolutional Neural Network, Recurrent Neural Network with Long Short Term Memory networks. They help in obtaining intricate genomic data. This is achieved through the spatial relationship and sequential dependencies within the genetic elements. The large-scale genomic datasets are trained which helps to learn the differentiation between the driver and passenger mutations. The genetic alterations provide information regarding the cancer progression. The deep learning model helps in the identification of desperate mutations with exclusive patterns that contribute to tumor development. The important aspect involves the prediction of prognosis. This includes the model to train and learn from the cumulative knowledge of patients in accordance with the genomic profiles. An accurate prognosis and personalized treatments are obtained by analyzing certain parameters such as gene expression, gene mutation and clinical data structures. These optimization techniques help in the identification of complex relationships in the identification of therapeutical compounds.