Sentence examples for sequence autocorrelation from inspiring English sources
Sequence driven features, sequence mapped features and sequence autocorrelation features were tested with ensemble learners and their performances were compared.
They include amino acid composition, dipeptide composition, sequence autocorrelation descriptors, sequence coupling descriptors, and the descriptors for the composition, transition and distribution of hydrophibicity, polarity, polarizibility, charge, secondary structures, and normalized Van der Waals volumes.
The second group consists of sequence mapped features, which are generated by mapping amino acids into sub groups and the third group contains sequence autocorrelation features, which are obtained from calculations based on three types of spatial autocorrelation (Moreau-Broto, Moran and Geary).
Sequence autocorrelation-based features also assume that "the disturbances in each area are systematically related to those in adjacent areas" [37].
Sequence autocorrelation-based features are based on the Tobler's first law of geography – "everything is related to everything else but nearby things are more related than distant things" [36].
The core algorithm for discovering SSR patterns from genome sequences employs autocorrelation methodology.
Details of each procedure of these two phases are described in the following sections: The core algorithm for discovering SSR patterns from genome sequences employs autocorrelation methodology.
The present study of behavioural sequences applies autocorrelations of consecutive responses as a means of studying moment-to-moment dynamics in responding.
These methods may be divided into two groups: one dealing with the magnitude of the autocorrelation sequence whilst the other works on the phase of the autocorrelation sequence.
Estimation of noise autocorrelation sequence in each utterance and subtracting it from the speech signal autocorrelation sequence in each frame of the utterance (more details of parameter settings will be found in Subsection 5.3).
This algorithm combines the use of the enhanced autocorrelation sequence of the noisy speech, and the spectral peaks found from the autocorrelation sequence, as they are known to convey the most important information of the speech signal [14].
