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In bioinformatics, protein multiple sequence alignment (MSA) and phylogenetic tree construction are among the major problems for which many algorithms have been developed to improve the accuracy the results. However, finding the best algorithm among the available ones remains a challenging task since the efficiency of an algorithm is closely related to the characteristics of the input sequences. Moreover,...
Protein structure prediction is a long standing problem in the filed of structural bioinformatics. To address this problem, on the basis of Monte Carlo (MC), a double estimation of distribution guided sampling algorithm for de-novo protein structure prediction (DED) is proposed. Serval MC trajectories are launched simultaneously at the begining of DED. And two probability distribution are designed...
Protein Structure Prediction (PSP) is an NP-hard optimization problem that has been solved by many existing algorithms. Simply, it can be thought of a process of predicting the native 3D structure from its amino acid sequence. Chemical Reaction Optimization (CRO) is a recent metaheuristic algorithm that has been applied to many well-known problems and has shown better performance compared to the existing...
A major challenge in protein-protein docking is the distinction between near-native and decoy complex predictions. It has been shown that near native solutions are usually located at the bottom of deep and densely populated funnels in the binding energy plot of the complex. Thus exploration, whether the energy plot of the vicinity of a docking solution is “funnel like”, can serve as a validation of...
Referring to ab-initio protein structure prediction, a replica exchange based differential evolution searching method (REDE) was proposed. Firstly, employing fragment assembly technique to reduce the dimension of protein conformational space effectively, thus avoiding the entropy effect in searching. Additionally, every replica layer equipped with a conformation population, updating the population...
In general, the gap is broadening rapidly between the number of known protein sequences and the number of known protein structural classes. To overcome this crisis, it is essential to develop a computational prediction method for fast and precisely determining the protein structural class. Based on the predicted secondary structure information, the protein structural classes are predicted. To evaluate...
Protein-Protein Interaction and Prediction of neighboring residues is challenging. This paper revitalize, intent to resolve and to associate with the interaction of multiple proteins that indicates the multiple protein network. But here the interrupt deals with the neighboring residues that is amino acids. The algorithm that used is directed for progression to their neighboring residues and introduce...
The reduced cost of the next generation sequencing technologies provides opportunities to study non-model organisms. However, one challenge is the large volume of data generated and, thus, the need to use automated approaches to annotate these data. Machine learning algorithms could provide a cost-effective solution but they need lots of labeled data and informative features to represent these data...
Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named label distribution learning (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which...
The detection of biologically meaningful clusters in protein interaction networks is crucial in systems biology. Among its applications, it can enable the identification of protein complexes. Notwithstanding significant advances, the detection of meaningful clusters faces important challenges, including the need to aid researchers in the prioritization of hundreds or even thousands of clusters. To...
Proteomics is currently driven by mass spectrometry. For the analysis of tandem mass spectra many computational algorithms have been proposed. There are two approaches, one which assigns a peptide sequence to a tandem mass spectrum directly and one which employs a sequence database for looking up possible solutions. The former method needs high quality spectra while the latter can tolerate lower quality...
Simulated annealing (SA) is one of the popular approaches to predict protein structures. SA is prohibitive because it usually consumes much computing time and is likely to fall into local minimum points. We proposed a parallel SA algorithm based on a Graph Process Unit (GPU) technique to improve the efficiency and accuracy of the protein structure prediction. First, we analyze the SA algorithm based...
The analysis of the whole set of molecular interactions in an organism, often referred to as interaction networks, is becoming an important research area. A main approach for such analysis resides on the application of clustering techniques to such networks. The meaning of discovered clusters, (i.e. highly interconnected regions), is strictly related to the type of networks. For instance in protein-protein...
This paper presents a novel priority based data mining algorithm using improved K-means clustering for detecting proteins sequence from dataset of frequent item set. The priorities are set depending on the number of hits (counts) from the dataset concurrently using the concept of multiprocessing. Which dynamically changing for a period of time series, a novel algorithm is used for classification and...
Identifying modules in protein-protein interaction (PPI) networks is important to understand the organization of the cellular processes. In this paper, an improved algorithm based on affinity propagation (AP) is proposed. We embed AP in our algorithm by utilizing AP to find the candidate overlapping vertices and keep those satisfying our filter condition. We apply our algorithm to S. cerevisiae PPI...
Protein structure prediction, known as an NP-complete problem, is one of the basic problems in computational biology. To get an efficiency approach of protein structure prediction with Toy model, a new algorithm structure based on quantum-behaved particle swarm optimization (QPSO) structure is suggested, which is named as multiple-layer QPSO (MLQPSO). In this structure, population of each generation...
The functional knowledge of cancer proteins and cancer pathways is currently limited and not detailed enough, remaining as a major hurdle to cancer studies. Particularly, many cancer proteins are only annotated to high-level general GO categories. Here, we apply an efficient algorithm, by constructing function-specific protein-protein interaction sub-networks, to find finer functions of the cancer...
To establish the analytical calibration models with strong prediction ability and robust performance, it is very important to eliminate the interference information in diary product measurement using near-infrared (NIR) spectroscopy techniques. In this paper, five correction methods including extended multiplicative scatter correction, standard normal variate, orthogonal signal correction, similar...
Neurotoxins can be divided into presynaptic neurotoxins and postsynaptic neurotoxins based on their mechanism of action. The two neurotoxins have important application in basic research and drug design. Presynaptic neurotoxins have been used for treatment of migraine headache and cerebral palsy. Therefore, the successful prediction of these two neurotoxins is becoming very important in recent years...
Immune algorithm is a rising algorithm which simulates the organism immune system by computer. One of the immune algorithms named clonal selection algorithm is widely used due to its adaptability, implicit parallelism and diversity. A fitness function is constructed in this paper by using protein folding restrictions, such as amino acids' hydrophobicity rule, protein's secondary structure folding...
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