Results 21 to 30 of about 23,619,888 (317)
Tomography, 2023 Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume
Chenglin Zhu +11 more
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COPA—cancer outlier profile analysis [PDF]
Bioinformatics, 2006 Abstract Summary: Chromosomal translocations are common in cancer, and in some cases may be causal in the progression of the disease. Using microarrays, in which the expression of thousands of genes are simultaneously measured, could potentially allow one to detect recurrent translocations for a particular cancer type.
James W, MacDonald, Debashis, Ghosh
openaire +2 more sources
The efficiency of different outlier detection approaches in geodetic networks: case study for Pobednik statue [PDF]
Geodetski Vestnik, 2018 In the paper the efficacy of two different approaches for outlier detection in geodetic networks is analysed on a test example of a control network for geodetic monitoring of the Pobednik statue in the Kalemegdan Fortress in Belgrade by applying the mean
Mehmed Batilović +5 more
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Detection of outliers in processing of small size data
Tekhnologiya i Konstruirovanie v Elektronnoi Apparature, 2016 This article describes the criteria for detection of outliers power depending on a small size sample. Removing outliers is one of the stages of signals pre-processing.
B. C. Попукайло
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Frontiers in Genetics, 2020 Domestication to captive rearing conditions, along with targeted selective breeding have genetic consequences that vary from those in wild environments.
Maria G. Nayfa +10 more
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RODA: A Fast Outlier Detection Algorithm Supporting Multi-Queries
IEEE Access, 2021 Outlier detection is an important task in the field of big data analysis. The technology has been extensively used in network security, sensor data analysis, public health and so on.
Xite Wang, Jiafan Li, Mei Bai, Qian Ma
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Outlier Mining Methods Based on Graph Structure Analysis [PDF]
, 2019 Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms.
Almeira, Nahuel +2 more
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Collabra: Psychology, 2018 Recently, Brummelman, Thomaes, Orobio de Castro, Overbeek, and Bushman (2014: Study 3) demonstrated that inflated praise benefits challenge seeking of children with high self-esteem, but harms challenge seeking of children with low self-esteem.
Peter P. J. L. Verkoeijen +2 more
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Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases. [PDF]
, 2017 PURPOSE: The purpose of this study was to apply statistical metrics to identify outliers and to investigate the impact of outliers on knowledge-based planning in radiation therapy of pelvic cases.
Aggarwal +23 more
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Detecting outlier samples in microarray data [PDF]
, 2009 In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray ...
Hung, YS, Shieh, AD
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