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Computational prediction of protein structural class based solely on sequence data remains a challenging problem in protein science. Existing methods differ in the protein sequence representation models and prediction engines adopted. In this study, a powerful feature extraction method, which combines position-specific score matrix (PSSM) with auto(More)
Apoptosis proteins are very important for understanding the mechanism of programmed cell death. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on amino acid substitution matrix and auto covariance transformation, we introduce a new sequence-based model, which(More)
Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Thus, annotating the function of apoptosis proteins is of significant value. Since the function of apoptosis proteins correlates with their subcellular location, the information about their subcellular location can be very useful in understanding(More)
Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative(More)
A complexity-based approach is proposed to predict subcellular location of proteins. Instead of extracting features from protein sequences as done previously, our approach is based on a complexity decomposition of symbol sequences. In the first step, distance between each pair of protein sequences is evaluated by the conditional complexity of one sequence(More)
Phycodnaviruses are algae-infecting large dsDNA viruses that are widely distributed in aquatic environments. Here, partial genomic sequences of four novel algal viruses were assembled from a Yellowstone Lake metagenomic data set. Genomic analyses revealed that three Yellowstone Lake phycodnaviruses (YSLPVs) had genome lengths of 178,262 bp, 171,045 bp, and(More)
The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score(More)
DNA computing provides a promising method to solve the computationally intractable problems. The n-queens problem is a well-known NP-hard problem, which arranges n queens on an n × n board in different rows, columns and diagonals in order to avoid queens attack each other. In this paper, we present a novel parallel DNA algorithm for solving the n-queens(More)
Knowledge of structural class plays an important role in understanding protein folding patterns. As a transitional stage in recognition of three-dimensional structure of a protein, protein structural class prediction is considered to be an important and challenging task. In this study, we firstly introduce a feature extraction technique which is based on(More)
Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC)(More)