Contemporary biology produces huge data sets using genomics, transcriptomics, proteomics, and metabolomics-also referred to as omics sciences. Although these datasets provide important biological information, they are large and complex, and therefore, they cannot be analyzed manually. Herein lies the role of AI-omics, which entails the integration of artificial intelligence and multi-omics data to identify the biological patterns that were not evident.
The algorithms of machine learning are capable of combining information across various omics layers to detect disease signatures, predict drug responses, and also new therapeutic targets. Cancer research is an area where AI-omics is particularly effective, as it assists in categorizing tumors, making predictions of treatment results, and determining the specific biomarkers of a patient. AI-omics is faster, less expensive, and time-saving in drug discovery as it expedites target discovery and predicts toxicity. It also promotes personalized medicine, whereby therapies are tailored according to the molecular profile of an individual, as opposed to the one-size-fits-all therapy.
Although it promises things, AI-omics is largely reliant on quality data and prudent interpretation. To ensure that the computational predictions are converted to practical therapy, biological validation is still required.
MBH/PS