Researchers at the Bloomberg-Kimmel Institute for Cancer Immunotherapy at the Johns Hopkins Kimmel Cancer Center have developed DeepTCR, a software package that employs deep-learning algorithms to analyze T-cell receptor (TCR) sequencing data, according to a news release from the Johns Hopkins.
T-cell receptors are found on the surface of immune T cells. These receptors bind to certain antigens, or proteins, found on abnormal cells, such as cancer cells and cells infected with a virus or bacteria, to guide the T cells to attack and destroy the affected cells.
The research was published in Nature Communications.
Deep learning is a form of artificial intelligence that roughly mimics the workings of the human brain in terms of pattern recognition.
DeepTCR is a comprehensive deep-learning framework that includes both unsupervised and supervised deep learning models that can be applied at the sequence and sample level.
DeepTCR will enable investigators to study the function of the T-cell immune response in basic and clinical sciences by identifying the patterns in the receptors that confer the function of the T cell to recognize and kill pathological cells.
The software package, which employs a type of deep-learning architecture called a convolutional neural network, provides users the ability to find T-cell sequencing patterns that are relevant to a specific exposure, like a flu infection, a cancer or an autoimmune disease.
