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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom