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Path based fingerprints can be used for both substructure and similarity screening.
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In fact, fingerprints can be found on many objects.
The fingerprints can be used for exploring the chemical space based on their Euclidian distance or Tanimoto coefficients, and can also be used as inputs of various machine learning classifiers to predict various biological activities of compounds [ 13].
Hashed fingerprints can be generated as binary, recording the presence or absence of each substructure, or count based, recording the number of occurrences of each substructure.
Scanned fingerprints can be viewed on a computer monitor.
The words to be matched are stored as fingerprints in a dictionary so that sampled word fingerprints can be compared against them later.
Latent Fingerprints can be discovered by various methods.
Police can fingerprint people who are arrested, and those fingerprints can be used to charge people with other crimes.
Consensus queries using a single fingerprint can be several times faster than a consensus based on scores (Table 5).
Based on these standard criteria, our isolates K-1 and K-2 that exhibit the same molecular fingerprint can be considered as belonging to the same "outbreak clone" that is rapidly expanding in the study area by extensive transmission [22].
How do you classify fingerprints so that an unknown fingerprint can be efficiently matched with a card in a file?
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