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In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins.
In this step, we also explore the effective value for distance factor (K P ) in segmented auto covariance feature extraction method as well as segmentation factor (F P ) in segmented distribution method.
In this study, we propose segmented auto covariance and segmented distribution feature extraction methods to capture more local sequence order information from evolutionary and structural profiles.
In the present study, we extend the concept of segmented distribution features as described in the previous subsection to compute the auto covariance features from the segmented protein sequence.
We also repeat this experiment to explore the impact of segmentation factor F P in segmented distribution feature extraction method.
First, for the PSSM, to extract the segmented distribution feature group (PSSM-SD), we compute the total sum of substitution probability of the j column of the PSSM.
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The segment distribution information.
Fig. 1 Segment distribution in a cluster.
Additionally, each peer stores segments based on the proposed segment distribution scheme to reduce server stress.
The segment distribution information allows the picking of related segments from all the cloud storage services.
Each isoform displays a specific nephron segment distribution with a distinct subcellular localization.
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