Similarly, to detecting an illness on a genomic basis, AI can assist in determining which genes have been impacted by detrimental mutations to target them with gene therapy. DNA repair following modification is another area where AI could be used.
FREMONT, CA: Genome editing is a delicate yet powerful finding. The same can be said for artificial intelligence (AI), science with an ever-growing list of interdisciplinary applications (from receiving hotel advertisements after buying travel online to determining an individual's personality based on their visual features).
However, where do these two concepts meet? One of the primary concerns with CRISPR-Cas9-based technologies is accuracy and safety, as errors can have significant and dangerous consequences for an individual's genome. By taking what is already known and applying it to produce realistic and well-informed predictions, machine learning has the potential to significantly improve patient outcomes and reduce the risk of ineffective treatment and incorrect diagnosis in patients with genetic illnesses.
AI has two primary applications in genetics: identifying dangerous genes and treating disease. Consider how the identification application operates.
Analyzing the massive quantity of data contained in a single person's DNA is an incredibly tedious and time-consuming task for human beings. This analysis can be significantly improved in terms of efficiency and accuracy by leveraging machines for their intended purpose—to make tedious activities easier.
By comparing the various gene expression levels in malignant and normal tissue samples from a patient diagnosed with cancer, predictions can be made regarding which genes in the patient's DNA have been altered. The algorithms would be trained and make these predictions by comparing the frequency of expression of a gene in a cancer sample to that of the same gene in a normal sample, adding new details with each new set of data.
Additionally, by utilizing 3D imaging, AI can find genetic abnormalities within tumors. For instance, one method can detect the presence of glioma with a high degree of accuracy (almost 97 percent) utilizing a patient's brain scans. Machines can detect the existence of a mutation using techniques like deep learning and neural networks, allowing doctors to treat patients more effectively without the requirement for a tissue sample from a biopsy or the risk associated with surgery. Machine learning opens up great potential for automating the diagnosis of diseases such as cancer through these procedures.