ML-powered gene therapy promises to be a ray of hope for patients with rare genetic diseases who cannot be treated using existing drugs and treatments.
A major argument in favor of gene editing is its potential to cut out disease-causing genes. However, while technologies have come a long way, the risk of error remains significant, and safety must be a top priority for gene editing to evolve. AI and Machine learning algorithms are useful in finding where the alteration must be made and how to ensure that the DNA is repaired properly, reducing the potential for errors throughout the process. Similar to diagnosing disease using a genomic basis, AI can find which genes have been impacted by harmful mutations to be targeted in gene therapy. So, here is how AI and machine learning can help gene therapy.
Despite being a radical treatment methodology, gene therapy's success lies in ensuring the therapeutic gene is particularly targeted to the right cells and the right tissue. If poorly executed, gene therapy could encourage harmful mutations in the DNA, resulting in health problems for the patient. In addition to cell targeting, other hurdles facing gene therapists include inadvertently integrating the gene into germline cells and accurately assessing the body's natural immune responses to viral vectors that carry the gene-modifying DNA payloads.
A key remedy to address all of the above challenges is genome sequencing. This technique breaks down and sequences DNA components to better understand their function within the overall genetic code. However, analyzing this wealth of information through legacy statistical techniques can be slow, resource-intensive, and daunting. This is where Machine Learning systems are proving to be valuable. ML provides a learning opportunity from statistical data and performing actions based on the learnings. By using ML, the analysis of complex data can be achieved in a faster and effective way.
ML accelerates the analysis of sequenced data and also predicts the genetic alterations associated with a particular disease. This helps mitigate the time and effort needed in the process of developing precision medicine. Algorithms are developed based on patterns identified in large data sets, translating to human models to understand their effect.