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Leemput et al. [58] designed an automated method by using an intensity-based tissue classification and a stochastic model for detection of MS lesions from 3-D images.

Our aim is to develop a regression model for detection of major genes controlling phenotypic variance that can be applied genome wide.

In conclusion, we have developed a regression model for detection of major loci controlling phenotypic variance, which can be applied on a wide range of experimental crosses such as backcross, F2, and MAGIC lines such as the CC.

We performed a cross-sectional analysis of data from a national colonoscopy screening programme to derive and validate a risk prediction model for detection of advanced colorectal neoplasia.

Another potential application of a model for the detection of advanced colorectal neoplasia is to guide practical recommendations for mass screening; however, this application would require a model with high discriminatory power.

We evaluated if a construct of the α1β1 catalytic domain of sGC could be used as a model for the detection of binding to sGC.

The 3D NiO/PANI/ZnO heterostructure is then chose as a model for electrochemical detection of dopamine, uric acid and ascorbic acid.

We propose a model for the detection of both motion discontinuities and occlusion regions using different mechanisms at distinct processing stages.

A model for the detection of odors by insects, and their reaction thereupon.

We have reported a model for the detection of σ1R antagonist activity, and S1RA is likely the most potent and pure σ1R antagonist available (55).

Thickness measurements, together with texture features from image data within the layer boundaries, are used to build up a model for the detection of pathologic retina abnormalities like fluid filled regions.

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