Because RNA sequencing allows for both the identification and relative quantification of RNAs for thousands of genes and regulatory RNAs, the resulting datasets are extremely large and complex. Therefore, powerful computing methods are required for the analysis and interpretation of the data. Employing deep learning techniques and machine learning algorithms—forms of artificial intelligence—in the data analysis enables meaningful and data interrogation and interpretation.
Additionally, these techniques can learn from past data it encountered to enhance the accuracy of its data predictions! In these RNA datasets, there will be differential expression of RNAs related to an outcome or biological activity of interest; however, there will also exist many RNA changes that are unrelated. Artificial intelligence methodologies can differentiate these by applying specific criteria and performing advanced mathematical analyses to discover patterns in the RNA sequencing dataset that are connected with various activities or outcomes.
Once these patterns are identified, machine learning algorithms are trained to recognize these patterns to identify unique RNA signatures that indicate particular outcomes or biological processes! Over time, as new data is included in the models, the algorithm is enhanced from this new learning and the RNA signatures become increasingly robust. Artificial intelligence can also be trained to incorporate additional clinical factors into the analysis. Deep learning techniques and machine learning algorithms are ideal for investigating RNA sequencing data because they perform analyses of the data. And machine learning algorithms are currently being developed in other areas of the medical landscape to make prognoses or diagnoses.
These computational methods make indepth data analysis easier and lend the opportunity to discover biological systems previously unknown to be involved in the graft rejection or tissue healing process. Artificial intelligence can offer greater insights into non–immune system-mediated mechanisms of graft tolerance, such as cellular stress and cell or tissue repair.
