title: Discriminative motif discovery via simulated evolution and random under-sampling.
abstract: Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.
authors.affiliation: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Song
authors.affiliation: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D002451:Cell Compartmentation; D002462:Cell Membrane / Q000737:chemistry; D002467:Cell Nucleus / Q000737:chemistry; D003198:Computer Simulation; D017124:Conserved Sequence; D003600:Cytosol / Q000737:chemistry; D057225:Data Mining / Q000706:statistics & numerical data*; D005057:Eukaryotic Cells / Q000166:cytology / Q000378:metabolism; D006801:Humans; D008390:Markov Chains; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
chemical_list: D011506:Proteins