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Relation extraction is a challenging task in biomedical text mining due to the complex of sentences in the biomedical literature. In this paper, we address multi-class relationship extraction problem from biomedical literature using Maximum Entropy model with simple word features. The proposed method is applied to extract the protein-protein interactions. Experiments show the method achieves an accuracy...
Protein remote homology detection and fold recognition are central problems in bioinformatics. In this paper, two kinds of profile-level building blocks of protein sequences, binary profiles and N-nary profiles, are presented, which contain the evolutionary information of the protein sequence frequency profile. The two building blocks are applied for protein remote homology and fold detection tasks...
Protein complexes are key modules to perform protein functions within protein-protein interaction (PPI) network. Protein complexes are determined by both topological and biological properties. The information from protein primary sequence can help to understand principles of cellular organization and function of complexes. In this paper, a novel method for detecting protein complexes from protein...
Identification of long disordered regions in protein sequence is important for understanding protein function. In this work, a class of novel propensities at profile level is presented, namely, the order profile disorder propensities, which use the evolutionary information of profile for protein long disorder prediction. These propensities, combined with position-specific scoring matrices, are inputted...
Protein remote homology detection is a central problem in bioinformatics. In this study, we present a novel building block of proteins called order profiles (OP) to use the evolutionary information of the protein sequence frequency profiles and apply this novel building block to remote homology detection. Order profiles contain the evolutionary information extracted from the protein sequence frequency...
Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like...
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