Optimizing DNA Repair Mechanisms in Carcinogenesis Prevention Using the DNARepairNet Algorithm

Abstract

DNA repair systems thinks as the body’s personal spell-check for genes, catching typos that could one day turn into cancer. When the spell-check glitches, the mistakes start stacking up, and that’s when tumors can start. Finding a way to boost this repair spell-check might stop cancer well before it can start breathing. In this study, we tested DNARepairNet a savvy program that fine-tunes the DNA repair crew to see whether it can rev up the cells’ defenses and shift the odds away from a cancerous future. DNARepairNet first finds the weak spots in a repair system by mapping and modeling the repair networks; it then recommends ways to prevent the harmful changes that cancer cells hoard. We looked closely at three key repair routes: base excision repair (BER), nucleotide excision repair (NER), and double-strand break repair (DSBR). Each is vital for the cell’s guards against damage from the outside world and from normal cell processes. DNARepairNet relies on machine learning to predict how well each repair choice will work, finally leading to the most promising strategies. The results we collected reveal that DNARepairNet can streamline DNA repair pathways, slashing mutation rates and supercharging cellular repair machinery. This gives us a new tool to intercept cancerous changes before tumors can start. These results show that DNARepairNet builds custom road maps for improving the tiny workers that protect our DNA, and that these smart tweaks fit perfectly with the cancer prevention programs we already have. The study highlights how clever computer programs deepen our grasp of how to stop cancer early, and it opens the door for future research to automate and upgrade gene therapies using designs that are guided entirely by data.