For example, Figure 4 shows the CMV histograms and distribution of fingerprints in the three subdatabases of FVC2000.
It is a widely accepted hypothesis that the fingerprint modality does not contain a sufficient amount of effective entropy to resist attacks exploiting the distribution of fingerprint features, specifically false-accept attacks.
In order to rationalize similarity value considerations, a statistical approach named the conditional correlated Bernoulli model is presented that models similarity scores based on the statistical distribution of fingerprint features in large compound databases.
Figure S2 Statistical model fits for Morgan based SEA on the random background data set create from ChEMBL 19. Figure S3 Z-score distribution (Morgan fingerprint) of the random background data set created from ChEMBL 19 database.
Open image in new window Fig. 1 Frequency distribution of fingerprint patterns in left and right thumbs of males and females Hausa population of Kano state, Nigeria. Figure 2 shows the box and whisker plot of minutiae counts of males and females among Hausa population of Nigeria.
Based on scaffold distribution, molecular-fingerprint similarities, and chemical-space distribution, this library has high structural diversity, supporting its utility as a screening tool.
Biasing occurs, because for overlapping force distributions of fingerprint unfolding and complex ruptures, unbinding events are more likely to take place without fingerprint unfolding if the two distributions are closer together.
They directly affect the distribution of sub-fingerprints in the fingerprint database and thus affect the efficiency of nearest sub-fingerprint retrieval.
A general pattern of BLAST hit distributions (a fingerprint) of the genomes of interest is computed, and BLAST hit weights of each single gene are compared to the general fingerprint.
Given the distribution of radio fingerprints on grids, we can effectively estimate the cell coverage, as shown in Figure 4.
