Sentence examples for detection and alignment of from inspiring English sources

Repeated amino acid sequences are characteristics of the silaffins and silacidin [ 41, 61], therefore we analyzed the clustered proteins using RADAR (Rapid Automatic Detection and Alignment of Repeats in protein sequences) software to detect if any repeats were present [ 62].

2. Bietz S, Rarey M. ASCONA: Rapid detection and alignment of protein binding site conformations.

Taken as a whole, these issues further emphasize the need to continue to improve algorithms for the detection and alignment of these interesting genomic changes.

Our framework is modular in nature and can be readily integrated with other methods for the detection and alignment of TEs in genomic sequences.

Missing values in LC/MS data arise because of imperfect detection and alignment of peak intensities or by true absence of expression.

Tandem repeats in BAP, BAP-like and other repetitive proteins were identified by using the XSTREAM (variable Sequence Tandem Repeats Extraction and Architecture Modeling, [ 38]) and the RADAR (Rapid Automatic Detection and Alignment of Repeats, [ 39]) algorithms.

The acquired raw data files were analyzed by the MarkerLynx application manager version 4.1 for peak detection and alignment, parameters of which were set as formerly reported [ 18].

Additionally, it can be used for processing of liquid chromatography/mass spectrometry (LC-MS) data for peak detection, peak picking, and alignment of glycans based on retention time, precursor m/z values, and observed intensities across multiple samples.

In this report, all experiments described hereafter were performed with a discriminatory model trained using all data from Datasets L and M. Because the proposed method is the first DS-specific detection and alignment approach, the quality of its structural alignments may serve as a standard for the evaluation of related future works.

This approach accomplishes detection and alignment implicitly by storing transformations of training images (called templates) rather than explicitly detecting and aligning faces at test time.

The approach makes use of three strategies found in biological systems: space-variant sensing, a spatio-temporal-frequency-based model of motion detection and the alignment of sensory-motor maps.