Results 201 to 210 of about 84,843 (251)
Advanced Science, EarlyView.Integrated single‐cell mass spectrometry reveals distinct lipidomic signatures that differentiate leader and follower phenotypes in migratory breast cancer cells. Phenotype‐specific alterations across various lipid classes, including fatty acids and phosphatidylcholines, underpin functional heterogeneity.
Xiaoyue Huang +6 more
wiley +1 more source
Engineered Optogenetic Circuits In Yeast with Self‐Sustained Outputs
Advanced Science, EarlyView.Optogenetic quorum‐sensing (OptoQS) circuit enables Saccharomyces cerevisiae to record transient light stimulus with self‐sustained outputs. Panel a indicates the design of the OptoQS circuit based on G‐protein coupled receptor (GPCR) signaling cascade. Panel b indicates the mode of action of surrogate messenger‐mediated signal transmission at the cell
Cong Fan +7 more
wiley +1 more source
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Neighborhood outlier detection
Expert Systems With Applications, 2010KNN (k nearest neighbor) is widely discussed and applied in pattern recognition and data mining, however, as a similar outlier detection method using local information for mining a new outlier, neighborhood outlier detection, few literatures are reported on.
Duoqian Miao, Yumin Chen
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Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang 0001 +2 more
+4 more sources
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang 0001 +2 more
+4 more sources
WIREs Data Mining and Knowledge Discovery, 2011
AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih-Ling Tsai
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AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih-Ling Tsai
openaire +2 more sources
2013 Information Theory and Applications Workshop (ITA), 2013
The following outlier detection problem is studied in a universal setting. Vector observations are collected each with M coordinates. When the i-th coordinate is the outlier, the observations in that coordinate are assumed to be distributed according to the “outlier” distribution, distinct from the common “typical” distribution governing the ...
Yun Li 0007 +2 more
openaire +2 more sources
The following outlier detection problem is studied in a universal setting. Vector observations are collected each with M coordinates. When the i-th coordinate is the outlier, the observations in that coordinate are assumed to be distributed according to the “outlier” distribution, distinct from the common “typical” distribution governing the ...
Yun Li 0007 +2 more
openaire +2 more sources
Journal of Intelligent Information Systems, 2010
Outlier detection is a useful technique in such areas as fraud detection, financial analysis and health monitoring. Many recent approaches detect outliers according to reasonable, pre-defined concepts of an outlier (e.g., distance-based, density-based, etc.). However, the definition of an outlier differs between users or even datasets.
Cui Zhu +3 more
openaire +1 more source
Outlier detection is a useful technique in such areas as fraud detection, financial analysis and health monitoring. Many recent approaches detect outliers according to reasonable, pre-defined concepts of an outlier (e.g., distance-based, density-based, etc.). However, the definition of an outlier differs between users or even datasets.
Cui Zhu +3 more
openaire +1 more source
GeoInformatica, 2007
The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors.
Dechang Chen +3 more
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The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors.
Dechang Chen +3 more
openaire +1 more source