What Machine Learning And Immunology Can Learn From Each Other

The human immune system must be able to identify which are the body’s own cells and which are invaders. In the same way, a standard facial recognition program should be able to identify its target face from thousands of faces. This analogy is demonstrated by a new mathematical model. This model shows that the strategy for outsmarting the immune system is similar to the way to fool a pattern recognition system.

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Every time an immune cell encounters a new biomolecule (“ligand”), such as the protein coating of a virus, it must categorize the molecule and determine the appropriate response. But the system isn’t perfect; HIV, for example, can avoid setting off an immune response. “In immunology, you have unexpected ways of fooling immune cells,” says Paul François, of McGill University in Canada. He realized that there is a similarity between the way certain pathogens outsmart the immune system and the way a sophisticated hacker can fool image recognition software.

One way an immune cell distinguishes among molecules is by the length of time that they remain bound to a receptor on the cell’s surface. “Stickier” molecules are more likely to be foreign. One strategy for pathogens involves overwhelming immune cells with borderline molecules called antagonists that bind to receptors and have an intermediate stickiness. Surprisingly, this condition can reduce the likelihood that a cell will activate an immune response even against obvious foreign molecules.

Artificial neural networks—computer programs that in some ways mimic networks of brain cells—can be used for image recognition after they’ve been trained with a wide range of image data. But a carefully constructed and nearly imperceptible modification of an image could make a picture that appears to human eyes as a panda to be misclassified as a gibbon, for example.

In both of these cases, an attacker tries to cause a sorting system to misclassify an object. To formally demonstrate the analogy, François and his colleagues developed a mathematical model of decision making by a type of immune cell called a T cell. In the model, which is a variation on previous models, the cell’s many receptors are exposed to numerous ligands having a range of stickiness values. Next, the model assumes that a series of biochemical reactions results from the binding and unbinding events at the receptors, which leads to a score—a number that determines whether the cell will activate an immune response or not. (Biochemically, the score is the concentration of a certain biomolecule.) The biochemical reactions include both “gas pedals,” which raise the score and make an immune response more likely, and “brakes,” which reduce the score.

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(Image Credit: qimono/ Pixabay)

Source: neatorama

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