Search

This article presents a web server iPDA, which aims at identifying the disordered regions of a query protein. Automatic prediction of disordered regions from protein sequences is an important problem in the study of structural biology. The proposed classifier DisPSSMP2 is different from several existing disorder predictors by its employment of position-specific scoring matrices with respect to physicochemical properties (PSSMP), where the physicochemical properties adopted here especially take the disorder propensity of amino acids into account. The web server iPDA integrates DisPSSMP2 with several other sequence predictors in order to investigate the functional role of the detected disordered region. The predicted information includes sequence conservation, secondary structure, sequence complexity and hydrophobic clusters. According to the proportion of the secondary structure elements predicted, iPDA dynamically adjusts the cutting threshold of determining protein disorder. Furthermore, a pattern mining package for detecting sequence conservation is embedded in iPDA for discovering potential binding regions of the query protein, which is really helpful to uncovering the relationship between protein function and its primary sequence. The web service is available at http://biominer.bime.ntu.edu.tw/ipda and mirrored at http://biominer.cse.yzu.edu.tw/ipda.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Motivation: Protein sequence clustering has been widely exploited to facilitate in-depth analysis of protein functions and families. For some applications of protein sequence clustering, it is highly desirable that a hierarchical structure, also referred to as dendrogram, which shows how proteins are clustered at various levels, is generated. However, as the sizes of contemporary protein databases continue to grow at rapid rates, it is of great interest to develop some summarization mechanisms so that the users can browse the dendrogram and/or search for the desired information more effectively., Results: In this paper, the design of a novel incremental clustering algorithm aimed at generating summarized dendrograms for analysis of protein databases is described. The proposed incremental clustering algorithm employs a statistics-based model to summarize the distributions of the similarity scores among the proteins in the database and to control formation of clusters. Experimental results reveal that, due to the summarization mechanism incorporated, the proposed incremental clustering algorithm offers the users highly concise dendrograms for analysis of protein clusters with biological significance. Another distinction of the proposed algorithm is its incremental nature. As the sizes of the contemporary protein databases continue to grow at fast rates, due to the concern of efficiency, it is desirable that cluster analysis of a protein database can be carried out incrementally, when the protein database is updated. Experimental results with the Swiss-Prot protein database reveal that the time complexity for carrying out incremental clustering with k new proteins added into the database containing n proteins is O(n2betalogn), where beta congruent with 0.865, provided that k << n., Availability: The Linux executable is available on the following supplementary page.

Computer-generated holography (CGH) is a technique that aims to produce specific illumination patterns using coherent light. However, traditional CGH algorithms often struggle to balance computational speed with the accuracy of the generated hologram. To address this issue, a non-iterative algorithm named "DL-GSA" is proposed in this paper. DL-GSA combines unsupervised learning in machine learning with convolutional neural networks (CNN) to generate holograms with high accuracy and fixed computational complexity. Simulation experiments reveal that DL-GSA generates hologram patterns faster than the Modified Gerchberg-Saxton algorithm (MGSA) and double-phase retrieval algorithm (DPRA). Furthermore, the average accuracy of the generated holograms is higher than 95 %. These findings suggest that DL-GSA has the potential to significantly enhance the real-time hologram generation capabilities, making it a promising technique for future applications. [ABSTRACT FROM AUTHOR]

Abstract: In the field of proteomics, protein hierarchies based on sequence analysis have been extensively applied to automate the annotations of new proteins and facilitate the discovery and analysis of protein families. However, the presence of ambiguous similarities in large databases increases the difficulty of delivering protein family hierarchies with favorable sensitivity and specificity. This work develops the HomoClust algorithm that exploits the homogeneity of protein sequences in generating protein family hierarchies. HomoClust improves the clustering quality of traditional hierarchical clustering algorithms by adopting different clustering mechanisms for different levels of sequence similarity. With considering homogeneity detection during clustering process, HomoClust increases the sensitivity of protein clusters without a drop in high specificity. [Copyright &y& Elsevier]

Abstract: As the sizes of many contemporary databases continue to grow rapidly, incremental clustering has emerged as an essential issue for conducting data analysis on contemporary databases. An incremental clustering algorithm refers to an abstraction of the distribution of the data instances generated by the previous run of the algorithm and therefore is able to cope well with the ever-growing contemporary databases. There are two main challenges in the design of incremental clustering algorithms. The first challenge is how to reduce information loss due to the data abstraction (or summarization) operations. The second challenge is that the clustering result should not be sensitive to the order of input data. This paper presents the GRIN algorithm, an incremental hierarchical clustering algorithm for numerical datasets based on the gravity theory in physics. In the design of GRIN, a statistical test aimed at reducing information loss and distortion is employed to control formation of subclusters as well as to monitor the evolution of the dataset. Due to the statistical test-based summarization approach, GRIN is able to achieve near linear scalability and is not sensitive to input ordering. [Copyright &y& Elsevier]

In this paper we propose a technique that reconstructs high-resolution images with improved super-resolution algorithms, based on Irani and Peleg iterative method, and employs our suggested initial interpolation, robust image registration, automatic image selection and image enhancement post-processing. When the target of reconstruction is a moving object with respect to a stationary camera, high-resolution images can still be reconstructed, whereas previous systems only work well when we move the camera and the displacement of the whole scene is the same. [ABSTRACT FROM AUTHOR]

On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult to be detected using the modularity maximization approach. The major problem is that the overlapping structure barely be found by maximizing the fuzzy modularity function. In this paper, we firstly introduce a node weight allocation problem to formulate the overlapping property in the community detection. We propose an extension of modularity, which is a better measure for overlapping communities based on reweighting nodes, to design the proposed algorithm. We use the genetic algorithm for solving the node weight allocation problem and detecting the overlapping communities. To fit the properties of various instances, we introduce three refinement strategies to increase the solution quality. In the experiments, the proposed method is applied on both synthetic and real networks, and the results show that the proposed solution can detect the nontrivial valuable overlapping nodes which might be ignored by other algorithms. [ABSTRACT FROM AUTHOR]