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Figure 4 | BMC Bioinformatics

Figure 4

From: Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Figure 4

Application of the naive Bayesian method for the prediction of amyloidosis. Given a set of amyloidogenic and non-amyloidogenic derivatives of a single germline, it is possible to generate the probability that a mutation at a particular position would cause amyloidosis or not. Briefly, separate mutation propensities for amyloid (p AM ) and non-amyloid (p NAM ) formers are generated by counting the frequency of mutations per position. These fractions, as well as complements thereof (i.e. the probability that there will be no mutation in either an amyloid-former or non-amyloid-former at a particular position, in black) are subsequently used to compute the amyloidogenic and non-amyloidogenic probabilities of a test sequence. To calculate for the amyloidogenic probability of a test sequence, a probability is assigned to each of the n positions in the sequence based on the characteristic of that position (i.e. if it contains a mutation or not). For positions containing no mutations this probability is equivalent to q AM , q AM = 1 - p AM for position x. The probability for positions with mutations is equal to p AM . Non-amyloidogenic probabilities are calculated in a similar manner, but with the use of p NAM instead of p AM . To avoid multiplications by zero, the Laplace correction is used. A product of the probabilities is subsequently taken; if the product of amylodogenic probabilities is higher, the test sequence is classified as amyloidogenic.

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