Loss Inference
Loss Inference is a methodological contribution in paralogy inference that identifies and reconstructs the absence or deletion of structural, functional, or evolutionary elements within a system by distinguishing patterns of missing information from conserved or retained features.
24
Publications
953
Citations
85
Authors
33
Institutions
Publications per year
2017–2026
6
Parents
24
Passive network tomography using EM algorithms
Yolanda Tsang, Mark Coates, Robert Nowak · 2002 · 62 citations
Netscope: Practical Network Loss Tomography
Denisa Ghita, Hung Nguyen, Maciej Kurant et al. · 2010 · 56 citations · Full text
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85
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33
AT&T (United States)
Bedminster, United States
3 h-index
3 publications
118 citations

UNSW Sydney
Sydney, Australia
2 h-index
2 publications
56 citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
19 publications
861 citations

Shaanxi Normal University
Xi'an, China
2 h-index
11 publications
525 citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
2 h-index
6 publications
306 citations

Rice University
Houston, United States
2 h-index
4 publications
334 citations

University of Toronto
Toronto, Canada
2 h-index
5 publications
217 citations

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
6 publications
266 citations
1–8 of 33
1 publications · 25 citations
1
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Repository · 1 publications · 17 citations
1
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1 publications · 27 citations
1
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1 publications · 32 citations
1
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1 publications · 10 citations
1
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