A set intersection theorem and applications

1974 ◽  
Vol 7 (1) ◽  
pp. 199-211 ◽  
Author(s):  
John Freidenfelds
Keyword(s):  
Intersection Theorem ◽  
Set Intersection

scholarly journals A Finite Set Intersection Theorem

1981 ◽  
Vol 2 (2) ◽  
pp. 127-129 ◽  
Author(s):  
P. Frankl ◽  
I.G. Rosenberg
Keyword(s):  
Intersection Theorem ◽  
Set Intersection ◽  
Finite Set

scholarly journals A set intersection theorem and an application to a bipartite Ramsey theorem

2002 ◽  
Vol 242 (1-3) ◽  
pp. 297-298
Author(s):  
Mark Ramras
Keyword(s):  
Intersection Theorem ◽  
Ramsey Theorem ◽  
Set Intersection

scholarly journals Graphs of hyperbolic groups and a limit set intersection theorem

2017 ◽  
Vol 146 (5) ◽  
pp. 1859-1871 ◽  
Author(s):  
Pranab Sardar
Keyword(s):  
Hyperbolic Groups ◽  
Intersection Theorem ◽  
Limit Set ◽  
Set Intersection

An Extremal Set-Intersection Theorem

1974 ◽  
Vol s2-9 (2) ◽  
pp. 355-359 ◽  
Author(s):  
V. Chvátal
Keyword(s):  
Intersection Theorem ◽  
Set Intersection ◽  
Extremal Set

scholarly journals The limit set intersection theorem for finitely generated Kleinian groups

1996 ◽  
Vol 3 (5) ◽  
pp. 675-692 ◽  
Author(s):  
James W. Anderson
Keyword(s):  
Kleinian Groups ◽  
Intersection Theorem ◽  
Limit Set ◽  
Finitely Generated ◽  
Set Intersection

Secure Outsourced Private Set Intersection with Linear Complexity

2021 ◽  
Author(s):  
Sumit Kumar Debnath ◽  
Kouchi Sakurai ◽  
Kunal Dey ◽  
Nibedita Kundu
Keyword(s):  
Linear Complexity ◽  
Set Intersection ◽  
Private Set Intersection

scholarly journals Multi-Party Private Set Intersection: An Information-Theoretic Approach

2021 ◽  
Vol 2 (1) ◽  
pp. 366-379
Author(s):  
Zhusheng Wang ◽  
Karim Banawan ◽  
Sennur Ulukus
Keyword(s):  
Theoretic Approach ◽  
Information Theoretic ◽  
Set Intersection ◽  
Private Set Intersection ◽  
Information Theoretic Approach

scholarly journals TREASURE: Text Mining Algorithm Based On Affinity Analysis and Set Intersection to Find the Action of Tuberculosis Drugs against Other Pathogens

2021 ◽  
Vol 11 (15) ◽  
pp. 6834
Author(s):  
Pradeepa Sampath ◽  
Nithya Shree Sridhar ◽  
Vimal Shanmuganathan ◽  
Yangsun Lee
Keyword(s):  
Text Mining ◽  
Causes Of Death ◽  
Intersection Property ◽  
Research Papers ◽  
Bacterial Diseases ◽  
Mining Algorithm ◽  
Set Intersection ◽  
The World ◽  
Medical Researchers

Tuberculosis (TB) is one of the top causes of death in the world. Though TB is known as the world’s most infectious killer, it can be treated with a combination of TB drugs. Some of these drugs can be active against other infective agents, in addition to TB. We propose a framework called TREASURE (Text mining algoRithm basEd on Affinity analysis and Set intersection to find the action of tUberculosis dRugs against other pathogEns), which particularly focuses on the extraction of various drug–pathogen relationships in eight different TB drugs, namely pyrazinamide, moxifloxacin, ethambutol, isoniazid, rifampicin, linezolid, streptomycin and amikacin. More than 1500 research papers from PubMed are collected for each drug. The data collected for this purpose are first preprocessed, and various relation records are generated for each drug using affinity analysis. These records are then filtered based on the maximum co-occurrence value and set intersection property to obtain the required inferences. The inferences produced by this framework can help the medical researchers in finding cures for other bacterial diseases. Additionally, the analysis presented in this model can be utilized by the medical experts in their disease and drug experiments.

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