... mutual information describes how much the likelihood of observing b was influenced by the observation of a. The average mutual information is computed as follows: M�a, b� � �p�a, b�log a,b p�a, b� , =-=(1)-=- p�a�p�b� where p(a) and p(b), the probabilities of individual letters a and b, are estimated by the frequency with which they appear in the representative set of the FSSP alignments and their joint p...
...ngle number. 34 For two alignments, A and B (where A contains i aligned positions and B contains j aligned positions), the formula for shift-score is � i � A s�A�i�� � � s�B�j�� j � B shift-score � . =-=(3)-=- �A� � �B� For a given pair of alignments, the alignment shift of a residue at position i ranges from �N to N, where N is the length of the longer alignment. Shift is not used directly in the formula ...
...re, but is scaled to a related quantity called s(i), which is 1 for 0 shifts and approaches �ε (ε is typically set at 0.2) for large shifts: �� 1 � ε � � ε if shift(i) isdefined s�i� 1 � �shift�i�� . =-=(4)-=- 0 otherwise The scaling of shift(i) to s(i) avoids having a mean shift-error term dominated by large positive or negative shifts. With ε set at 0.2, shift-score ranges from �0.2 (worst) to 1.0 (best)...
... which neural network predicted b, P n(b), and the background or prior probability of b, P p(b). This log likelihood ratio, referred to as information gain G, is computed as follows: Pn�b� G � log2 . =-=(2)-=- Pp�b� For our purposes, we have found this measure advantageous over other measures, including percent residues predicted correctly (QN), segment overlap measure, and correlation coefficient. 28,24,2...