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Increasing confidence of protein interactomes using network topological metrics
Author(s) -
Jin Chen,
Wynne Hsu,
Mong Li Lee,
See-Kiong Ng
Publication year - 2006
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btl335
Subject(s) - false positive paradox , computer science , true positive rate , false positives and false negatives , scalability , computational biology , identification (biology) , protein–protein interaction , complement (music) , data mining , biology , artificial intelligence , genetics , gene , phenotype , botany , database , complementation
Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches.

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