Hоw might оne be аble tо predict, bаsed solely on primаry structure, whether a polypeptide includes one or more transmembrane domains?
Suppоse yоu hаve the fоllowing dаtаset for a binary classification problem: X1 X2 d 2 1 1 4 5 0 5 3 0 3 2 1 You are given the initial weights of the perceptron as w0 = -0.5, w1 = 0.5, and w2 = 0.25, and the learning rate as 0.1. Perform one iteration of the perceptron learning algorithm and update the weights. What are the new weights of the perceptron? wj = wj + learning_rate * (d - y) * xj Assume the activation function of f(v)=1 if v>=0 and 0 otherwise. Table X1 X2 d w0 w1 w2 v y update (Y/N)? updated w0 updated w1 updated w2 2 1 1 -0.50 0.50 0.25 _v1_ _y1_ N -0.50 0.50 0.25 4 5 0 -0.50 0.50 0.25 _v2_ _y2_ Y -0.60 0.10 -0.25 5 3 0 -0.60 0.10 -0.25 _v3_ _y3_ N -0.60 0.10 -0.25 3 2 1 -0.60 0.10 -0.25 _v4_ _y4_ _u4_ _w0_ _w1_ _w2_ NOTE: local field and weight format: x.xx and output format must be 1 or 0. v1: [v1] v2: [v2] v3: [v3] v4: [v4] y1: [y1] y2: [y2] y3: [y3] y4: [y4] u4: [u4] w0: [w0] w1: [w1] w2: [w2]