Identification of biomarkers for cancer by machine learning

Cancer is one of the deadliest diseases and early treatment is very important for successful recovery. However, early detection is required for early treatment, and currently, it is not an easy task for a lot of developing countries. Using biomarkers to fasten the diagnosis process may help to the solution of this since they can be detected through bodily fluids easily. Also, finding genetic factors leading to cancer is another task that is difficult to accomplish due to cancer being a complex disease as well as genetic data being a big data. Biomarkers detected from the healthy and cancerous tissue datasets may be informative for further research on this topic. In this thesis work, we developed an algorithm to detect biomarkers for cancer. Several machine learning models are used with the help of the PCA dimensionality reduction technique in the algorithm. The proposed algorithm is also converted to a publicly available software PREDIGENE which can be used by uploading a CSV file containing gene expression counts of healthy and diseased tissues. PREDIGENE is tested on two different cancer types (breast and liver) with six different datasets in total, three for each cancer type. The relevance and consistency of the results PREDIGENE returned are examined through a literature search and comparison with another publicly available tool developed for the same purpose. In conclusion, PREDIGENE, our tool to find cancer biomarkers through the usage of artificial intelligence, shows promising results when compared to the current literature.