Open AccessUncovering the rules for protein–protein interactions from yeast genomic data
Author(s) -
Jin Wang,
Chunhe Li,
Erkang Wang,
Xidi Wang
Publication year - 2009
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.0806427106
Subject(s) - protein–protein interaction , protein function prediction , computer science , computational biology , set (abstract data type) , function (biology) , bayesian probability , protein function , data mining , machine learning , artificial intelligence , biology , genetics , gene , programming language
Identifying protein–protein interactions is crucial for understanding cellular functions. Genomic data provides opportunities and challenges in identifying these interactions. We uncover the rules for predicting protein–protein interactions using a frequent pattern tree (FPT) approach modified to generate a minimum set of rules (mFPT), with rule attributes constructed from the interaction features of the yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regressions under various statistical measures. Our study indicates that mFPT outranks other methods in predicting the protein–protein interactions for the database used. We predict a new protein–protein interaction complex whose biological function is related to premRNA splicing and new protein–protein interactions within existing complexes based on the rules generated. Our method is general and can be used to discover the underlying rules for protein–protein interactions, genomic interactions, structure-function relationships, and other fields of research.