title: A Guaranteed Similarity Metric Learning Framework for Biological Sequence Comparison.
abstract: Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. The distance and similarity between two sequence are very important and widely studied. During the last decades, Similarity(distance) metric learning is one of the hottest topics of machine learning/data mining as well as their applications in the bioinformatics field. It is feasible to introduce machine learning technology to learn similarity metric from biological data. In this paper, we propose a novel framework of guaranteed similarity metric learning (GMSL) to perform alignment of biology sequences in any feature vector space. It introduces the (ϵ, γ, τ)-goodness similarity theory to Mahalanobis metric learning. As a theoretical guaranteed similarity metric learning approach, GMSL guarantees that the learned similarity function performs well in classification and clustering. Our experiments on the most used datasets demonstrate that our approach outperforms the state-of-the-art biological sequences alignment methods and other similarity metric learning algorithms in both accuracy and stability.
authors.forename: Keru
authors.initials: K
authors.lastname: Hua
authors.forename: Qin
authors.initials: Q
authors.lastname: Yu
authors.forename: Ruiming
authors.initials: R
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*