Melanoma Detection by Analysing Mutations in Gene DNA Sequences and Their Primary Protein Structures

Abstract

Melanoma is the deadliest form of skin cancer, whereas it has a metastases form when advanced into later stages. While skin cancers are most prominently seen in individuals with white skin, any individual can be diagnosed with skin cancers at any point in their life. Melanoma, mostly left untreated and undetected till its later stages make the patients’ lives be challenged, which has increased the importance of early-detecting. In this research, an effective approach is proposed for detecting Melanoma by analyzing gene DNA sequences of a subject, where the mutations are analyzed from nucleotide level up to the amino acid level. The research also consists of making sure the sequences are less fragmented when extracting, and also conducts a thorough analysis on the effect of various features such as gene, protein primary structure, age, tumor, tier, etc. to Melanoma with the help of machine learning algorithms. The obtained results are evaluated based on cross-validation and results from existing approaches.