Show simple item record

dc.creatorPark, Yungkien_US
dc.date.accessioned2016-10-28T19:50:28Z
dc.date.available2016-10-28T19:50:28Z
dc.date.issued2009-12en_US
dc.identifierdoi:10.15781/T2FB4WP75
dc.identifier.citationPark, Yungki. "Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences." BMC bioinformatics, Vol. 10, No. 1 (Dec., 2009): 1.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://hdl.handle.net/2152/43196
dc.description.abstractProtein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research. Results: Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests. Conclusions: The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.en_US
dc.description.sponsorshipen_US
dc.language.isoEnglishen_US
dc.relation.ispartofen_US
dc.rightsAdministrative deposit of works to Texas ScholarWorks: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access or the publisher allows a PDF version of the article to be freely posted online. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University.en_US
dc.subjectdomain-domain interactionsen_US
dc.subjectyeast saccharomyces-cerevisiaeen_US
dc.subjectshorten_US
dc.subjectpolypeptide sequencesen_US
dc.subjectinteraction networken_US
dc.subjectinteraction sitesen_US
dc.subjectinteraction mapen_US
dc.subjectwide scaleen_US
dc.subjectconservationen_US
dc.subjectbiologyen_US
dc.subjectpairsen_US
dc.subjectbiochemical research methodsen_US
dc.subjectbiotechnology & applied microbiologyen_US
dc.subjectmathematical & computational biologyen_US
dc.titleCritical Assessment of Sequence-Based Protein-Protein Interaction Prediction Methods that do not Require Homologous Protein Sequencesen_US
dc.typeArticleen_US
dc.description.departmentCenter for Systems and Synthetic Biologyen_US
dc.rights.restrictionOpenen_US
dc.identifier.doi10.1186/1471-2105-10-419en_US
dc.contributor.utaustinauthorPark, Yungkien_US
dc.relation.ispartofserialBMC Bioinformaticsen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record