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Deep Learning for Cancer Immunotherapy

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Introduction In my research, I apply deep learning to unravel molecular interactions in the human immune system. One application of my research is within cancer immunotherapy (Immuno-oncology or Immunooncology) – a cancer treatment strategy, where the aim is to utilize the cancer patient’s own immune system to fight the cancer. The aim of this post is to illustrates how deep learning is successfully being applied to model key molecular interactions in the human immune system. Molecular interactions are highly context dependent and therefore non-linear. Deep learning is a powerful tool to capture non-linearity and has therefore proven invaluable and highly successful. In particular in modelling the molecular interaction between the Major Histocompability Complex type I (MHCI) and peptides (The state-of-the-art model netMHCpan identifies 96.5% of natural peptides at a very high specificity of 98.5%). Adoptive T-cell therapy Some brief background…
Original Post: Deep Learning for Cancer Immunotherapy