MANet: A Deep Learning Framework for Multi-Cancer Microarray Analysis, Classification, and Visualization

Machine learning (ML) methods have been used much more frequently in recent years to extract gene expression data from microarray studies, especially in cancer research. Even after the continued interest in applying ML to scientific cancer research, there is still no universal approach for categorizing cancer microarray data. A system is needed that can detect and classify a normal profile and a cancer profile, specifying the type of cancer. Due to the variance and high dimensionality of microarray data, it is difficult to extract the relevant descriptors and provide insights that can be helpful in identifying cancer types and stages. In this paper, we proposed MANet: a methodology using cancer microarray data based on Deep Learning (DL) to classify 13 different types of cancers as well as normal profiles. To implement this methodology, we used a Curated Microarray Database (CuMiDa) that has 78 datasets for different types of cancers. Due to the diverse feature vectors for each dataset, we used Principal Component Analysis (PCA) for uniform feature engineering. Our single model has the capability to learn the patterns, cluster instances into their corresponding classes and classify the cancer. We also used the Uniform Manifold Approximation and Projection (UMAP) to visualise the instance separation on original data. Additionally, this UMAP visualises segregation done by our methodology in low dimensions. Using the proposed methodology, we achieved approximately 80% average accuracy, precision, recall, and F1 Score for 14 classes using a single model.