title: Analysing large biological data sets with an improved algorithm for MIC.
abstract: The computational framework used the traditional similarity measures to find out the significant relationships in biological annotations. But its prerequisites that the biological annotations do not cooccur with each other is particular. To overcome it, in this paper a new method Improved Algorithm for Maximal Information Coefficient (IAMIC) is suggested to discover the hidden regularities between biological annotations. IAMIC approximates a novel similarity coefficient on maximal information coefficient with generality and equitability, by bettering axis partition through quadratic optimisation instead of violence search. The experimental results show that IAMIC is more appropriate for identifying the associations between biological annotations, and further extracting the novel associations hidden in collected data sets than other similarity measures.
authors.forename: Shuliang
authors.initials: S
authors.lastname: Wang
authors.forename: Yiping
authors.initials: Y
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D016208:Databases, Factual; D066264:Datasets as Topic; D000069550:Machine Learning; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*