RNA Signatures

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 etc...

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— etc...

Defining RNA signatures associated with graft tolerance opens the door to identifying new biological markers and therapeutic targets, paving the way for personalized immunosuppressant therapy. By combining high-throughput RNA sequencing with advanced deep learning, researchers can uncover molecular patterns critical to understanding kidney graft tolerance. With these insights, machine learning algorithms can be trained to generate prognoses and diagnoses—helping to improve transplant outcomes using only a patient’s blood sample.

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