Defining RNA signatures associated with graft tolerance may also provide the opportunity to identify new biological markers or therapeutic targets in this process and allow for the personalization of immunosuppressant therapy Impact and clinical application. The combined power of high-throughput next-generation RNA sequencing and deep learning enables the gathering of molecular insights critical for understanding the mechanisms underlying kidney graft tolerance. With this knowledge, trained machine learning procedures can ultimately be utilized for making prognoses or diagnoses about kidney transplantations to improve patient outcomes—all from just the patient’s blood sample.

The transcriptome is highly dynamic and sensitive to environmental conditions, and RNA profiling enables real-time profiling of the RNAs expressed at the time of sample collection. From RNA profiling, information on which biological pathways are active may be deduced by measuring the amount of RNAs corresponding to genes or regulatory RNAs known to be involved in the pathways. RNA sequencing is a powerful technology that is highly sensitive and provides realtime information about the systems and cellular signaling affecting the transplant beyond just the immune system.

This can be invaluable to physicians during their management of patients undergoing kidney transplantation. For example, performing RNA profiling of a patient prior to kidney transplantation may provide insights on the tendency for a patient’s immune system to attack a graft and help guide immunosuppressant therapy dose levels and other related decisions. Profiling of a patient following a kidney transplant may offer realtime insights into their biological responses and signs of damage, such as inflammation, which gives clinicians actionable data to guide their patient’s treatment plan! Importantly, RNA profiling may serve as a more sensitive method of detecting damage that would have gone undetected otherwise and is important for both short- and long-term damage assessments. It is expected that RNA profiling of patients throughout their kidney transplant journey will provide important information that supports successful outcomes.

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.