1.
Protein Remote Homology Detection and Fold Recognition Based on Sequence-Order Frequency Matrix.
Liu, B, Chen, J, Guo, M, Wang, X
IEEE/ACM transactions on computational biology and bioinformatics. 2019;(1):292-300
Abstract
Protein remote homology detection and fold recognition are two critical tasks for the studies of protein structures and functions. Currently, the profile-based methods achieve the state-of-the-art performance in these fields. However, the widely used sequence profiles, like position-specific frequency matrix (PSFM) and position-specific scoring matrix (PSSM), ignore the sequence-order effects along protein sequence. In this study, we have proposed a novel profile, called sequence-order frequency matrix (SOFM), to extract the sequence-order information of neighboring residues from multiple sequence alignment (MSA). Combined with two profile feature extraction approaches, top-n-grams and the Smith-Waterman algorithm, the SOFMs are applied to protein remote homology detection and fold recognition, and two predictors called SOFM-Top and SOFM-SW are proposed. Experimental results show that SOFM contains more information content than other profiles, and these two predictors outperform other state-of-the-art methods. It is anticipated that SOFM will become a very useful profile in the studies of protein structures and functions.
2.
Multi-label learning with fuzzy hypergraph regularization for protein subcellular location prediction.
Chen, J, Tang, YY, Chen, CL, Fang, B, Lin, Y, Shang, Z
IEEE transactions on nanobioscience. 2014;(4):438-47
Abstract
Protein subcellular location prediction aims to predict the location where a protein resides within a cell using computational methods. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins and multi-location proteins. The latent concepts are extracted through feature space decomposition and label space decomposition under the nonnegative data factorization framework. The extracted latent concepts are used as the codebook to indirectly connect the protein features to their annotations. We construct dual fuzzy hypergraphs to capture the intrinsic high-order relations embedded in not only feature space, but also label space. Finally, the subcellular location annotation information is propagated from the labeled proteins to the unlabeled proteins by performing dual fuzzy hypergraph Laplacian regularization. The experimental results on the six protein benchmark datasets demonstrate the superiority of our proposed method by comparing it with the state-of-the-art methods, and illustrate the benefit of exploiting both feature correlations and label correlations.