Obviously, larger Recall t and Precision t imply better prediction performance of the method.
We discuss how these tools could be used to design the next generation of smart T cell precision therapeutics.
Denote the set of matched images returned by the retrieval system to be S and the set of ground-truth matched images from the database to be T, then precision is defined as Precision=frac{|S{{bigcap}}T|}{|S|} (21).
Then, at each confidence threshold t, average precision and recall is calculated across all targets.
At each position k in the ranked list T ζ, precision is calculated using Eq. 2 where N = k and n is the number of relevant images counted until cutoff k.
where B = I + T t Σ−1 Ñ T is the precision matrix, Σ is a block diagonal covariance matrix with blocks given by the covariance matrices of the UBM, I is the identity matrix, and Ñ and F indicate the zeroth- and first-order sufficient statistics, respectively, of an utterance [27, 29].
For the boundary node set the precision ( T P / ( T P + F P ) ) for this dataset is 0.58 and for the randomly selected group of nodes 0.33 (two significant figures given).
The recall is T P / (T P + F N) and the precision is T P / (T P + F P).
In experiment 1, for each site of cTBS application (AIP or control), RM-ANOVAs were performed on the reaction and movement times, relative MEP amplitudes and EMG values with C-T interval (4, 6, 8, 10, or T alone), grasp (precision grip or whole-hand grasp), and cTBS (pre or post) as within-subject factors.
To compare current algorithm with that proposed by Betel et al. (2007), 36 373 mouse piRNAs were taken as training positive set to predict the remaining 36 374 piRNAs, when t = 0, precision is 68.41%, and sensitivity is 99.31%; setting t = 2, precision is 95.53%, and sensitivity is 72.47%.
