DNA Sequencing-Induced Cancer Detection: A Representation Learning-Inspired Sustainable Transformation in Internet of Healthcare

The ability to explore genetic changes linked to carcinogenesis has revolutionized the field of cancer detection because of developments in DNA sequencing instruments. However, because of the enormous complexity and variety of the human genome, properly separating cancer-related mutations from benign genomic changes continues to be difficult. In this article, we present an approach for cancer detection utilizing DNA sequencing data that is inspired by representation learning. Our approach makes use of autoencoder neural networks and representation learning to identify significant patterns and distinguishing characteristics in DNA sequences. The approach can automatically extract and prioritize pertinent data related to modifications specific to cancer by learning a hierarchical representation of genomic data. We use large-scale datasets including cancer and non-cancer samples to train our model, allowing us to capture the full spectrum of genetic variants found in various populations. The autoencoder neural network architecture receives the preprocessed and converted numerical representations of the DNA sequences as input. The model learns to map the input sequences to a low-dimensional latent space through an iterative training procedure, which makes it easier to classify data into cancer and non-cancer groups. We test the effectiveness of our representation learning-inspired methodology using a substantial benchmark dataset that includes a wide range of cancer kinds. Our findings show that, when compared to conventional mutation-based cancer detection approaches, our method achieves improved accuracy, sensitivity, and specificity. Furthermore, when used on previously unexplored data, our method demonstrates robustness and generalizability, pointing to its potential for clinical applications.