ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins

Proto-oncogenes are the genes that have potential to transform normal cells into cancer as a result of mutations. They usually contain encoding proteins whose function is inhibit cell differentiation, stimulate division, and prevent death cells. While prognosis regarding proto-oncogene may occur at varying phases cancer, accuracy identification method always questionable. The standard procedure for detecting these involves in-vitro experimentations but it proves be very costly, time taking, laborious. This problem addressed by use computer-aided approaches established in studies encompassing methods computational biology bioinformatics. Early diagnosis crucial full recovery patient. Proto-oncogene an important biomarker helps identify onset specific type cancer. Keeping this mind, study proposes efficient methodology in-silico proto-oncogenes. predictor proposed computes position composition relevant statistical features incorporated pseudo-amino-acid (PseAAC) based on Chou’s 5-step rules. Subsequently, finds random forest classifier performs most accurate prediction proteins. was validated using 10 folds cross-validation, Jackknife testing, Self-Consistency, Independent set giving 95.44%, 97.17%, 99.8%, 96.41% results, respectively. These results imply model can play key role early aid scientists discovery mechanisms against