A Novel Random Forest-Based Model to Predicting Anticancer Peptides

According to the report by the international Agency for Rrecentesearch on Cancer (IARC) , approximately 18.6 million new cancer cases were estimated and the cancer is responsible for about 9.6 million deaths in 2018[1]. Additionally, because of the emergence of resistance to chemotherapeutic drugs or their non-selective activity, causingsevere side effects, current cancer therapies are less effective. In this regard, the discovery and development of novel anticancer agents are in urgent need. In between, in the recent decade, anticancer peptides (ACPs) are considered useful multifaceted molecules that may overwhelm the tumor chemical resistance and non-selectivetargeting of neoplastic-cells. the ACPs are positively charged sequences of short to medium size length of 5-30 amino acids and structurally diverse peptides of α-helices and β-sheets[2].