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The integration algorithm described in the Methods section is evaluated on the task of predicting protein functions for Saccharomyces cerevisiae.
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To apply selection method, a fitness function, which will be described in this section, is evaluated for each loop based on the workload of the loops.
Sections were evaluated on an inverted Leica DMI4000B microscope and digital images collected using an attached Leica DFC320 camera.
Sections were evaluated on a Zeiss-EM 900 electron microscope.
Both μCT slices and corresponding histological sections were evaluated on sagittal planes.
Immunostained sections were evaluated on scales devised by blinded individual, which ranged from 0 to 3 on the basis of pathology intensity.
The apoptotic events present on sections were evaluated on a scale of 0 to 4 with regard to presence or absence of apoptotic events as well as the scope of apoptotic regions.
Flow around the streamline box girder section is analyzed, and the effect of section details on the aerodynamic characteristics of bridge section is evaluated.
In this section, the proposed algorithm is evaluated on several key criteria; specifically, the sensitivity of the scan correction to localised distortions, the ability to correct small sample rotations and the fidelity of detail preservation at edges/boundaries.
In the Results section, the proposed method is evaluated on 15 microarray data sets - 6 breast cancer, 3 lung cancer, 3 gastric cancer and 3 renal cancer studies - grouped in pairs by phenotypic and class criteria.
In Section IV, the performance of SIFT is evaluated on radar images, and the trajectory estimation performance on experimental data is given in Section V.
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