International Journal of Bioinformatics Research and Applications最新文献

Single Nucleotide Polymorphism (SNP) is a mutation where, a single base in the DNA differs from the usual base at that position. SNPs are the marker of choice in genetic analysis and also useful in locating genes associated with diseases. SNPs are important and frequently occurring point mutations in genomes and have many practical implications. In silico methods are easy to study the SNPs that are occurring in known genomes or sequences of a species of interest during the post genomic era. There are many on-line and stand alone tools to analyse the SNPs. We intend to guide the reader with the software details such as algorithmic background, file requirements, operating system specificity and species specificity, if any, for the tools of SNPs detection in plants and animals. We also list many databases and resources available today to describe SNPs in wide range of organisms.

DNA microarray experiment unavoidably generates gene expression data with missing values. This hardens subsequent analysis such as biclusters detection which aims to find a set of co-expressed genes under some experimental conditions. Missing values are thus required to be estimated before biclusters detection. Existing missing values estimation algorithms rely on finding coherence among expression values throughout the data. In view that both missing values estimation and biclusters detection aim at exploiting coherence inside the expression data, we propose to integrate these two steps into a joint framework. The benefits are twofold; the missing values estimation can improve biclusters analysis and the coherence in detected biclusters can be exploited for accurate missing values estimation. Experimental results show that the bicluster information can significantly improve the accuracy in missing values estimation. Also, the joint framework enables the detection of biologically meaningful biclusters.

Missing data problem degrades the statistical power of any analysis made in clinical studies. To infer valid results from such studies, suitable method is required to replace the missing values. There is no method which can be universally applicable for handling missing values and the main objective of this paper is to introduce a common method applicable in all cases of missing data. In this paper, Bayesian Genetic Algorithm (BGA) is proposed to effectively impute both missing continuous and discrete values using heuristic search algorithm called genetic algorithm and Bayesian rule. BGA is applied to impute missing values in a real cancer dataset under Missing At Random (MAR) and Missing Completely At Random (MCAR) conditions. For both discrete and continuous attributes, the results show better classification accuracy and RMSE% than many existing methods.

Image segmentation algorithms are critical components of medical image analysis systems. This paper presents a novel and fully automated methodology for segmenting anatomical branching structures in medical images. It is a hybrid approach which integrates the Canny edge detection to obtain a preliminary boundary of the structure and the fuzzy connectedness algorithm to handle efficiently the discontinuities of the returned edge map. To ensure efficient localisation of weak branches, the fuzzy connectedness framework is applied in a sliding window mode and using a voting scheme the optimal connection point is estimated. Finally, the image regions are labelled as tissue or background using a locally adaptive thresholding technique. The proposed methodology is applied and evaluated in segmenting ductal trees visualised in clinical X-ray galactograms and vasculature visualised in angiograms. The experimental results demonstrate the effectiveness of the proposed approach achieving high scores of detection rate and accuracy among state-of-the-art segmentation techniques.

We introduce a new formulation for total variation minimisation in image denoising. We also present a linearly convergent first-order method for solving this reformulated problem and show that it possesses a nearly dimension-independent iteration complexity bound.

In recent times, the size of biological databases has increased significantly, with the continuous growth in the number of users and rate of queries; such that some databases have reached the terabyte size. There is therefore, the increasing need to access databases at the fastest rates possible. In this paper, the decision tree indexing model (PDTIM) was parallelised, using a hybrid of distributed and shared memory on resident database; with horizontal and vertical growth through Message Passing Interface (MPI) and POSIX Thread (PThread), to accelerate the index building time. The PDTIM was implemented using 1, 2, 4 and 5 processors on 1, 2, 3 and 4 threads respectively. The results show that the hybrid technique improved the speedup, compared to a sequential version. It could be concluded from results that the proposed PDTIM is appropriate for large data sets, in terms of index building time.

In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. (i) Transformation technique using Discrete Walsh-Hadamard Transform (DWHT); (ii) application of fuzzy logic control and (iii) building the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal.

Information on the directionality and structure of axonal fibres in neural tissue can be obtained by analysing diffusion-weighted MRI data sets. Several fibre tracking algorithms have been presented in the literature that trace the underlying field of principal orientations of water diffusion, which correspond to the local primary eigenvectors of the diffusion tensor field. However, the majority of the existing techniques ignore the secondary and tertiary orientations of diffusion, which contain significant information on the local patterns of diffusion. In this paper, we introduce the idea of perpendicular fibre tracking and present a novel dynamic programming method that traces surfaces, which are locally perpendicular to the axonal fibres. This is achieved by using a cost function, with geometric and fibre orientation constraints, that is evaluated dynamically for every voxel in the image domain starting from a given seed point. The proposed method is tested using synthetic and real DW-MRI data sets. The results conclusively demonstrate the accuracy and effectiveness of our method.

Homology models are increasingly used to determine structural and functional relationships of genes and proteins in biomedical research. In the current study, for the first time, we compared the TRPC6 gene in mouse and human. The protein encoded by this gene forms a receptor activated calcium channel in cell membrane. Defects in this gene have been implicated in a wide range of diseases including glioblastomas. To determine the structural similarities in mouse and human TRPC6, we used standard bioinformatics tools such as fold prediction to identify the protein 3D structure, sequence-structure comparison, and prediction of template and protein structure. We also used glioblastoma cell line U373MG and human glioblastoma tumour tissues to study the expression of TRPC6 in disease conditions to implicate this gene in pathological ailment. Based on the results we conclude that human TRPC6 contains 90% identity and 93% similarity with mouse TRPC6, suggesting that this protein is well conserved in these two species. These isoforms likely demonstrate similar mechanisms in regulating gene expression; thus TRPC6 studies in mice may be extrapolated to humans.

In genetic engineering, conventional techniques and algorithms employed by forensic scientists to assist in identification of individuals on the basis of their respective DNA profiles involves more complex computational steps and mathematical formulae, also the identification of location of mutation in a genomic sequence in laboratories is still an exigent task. This novel approach provides ability to solve the problems that do not have an algorithmic solution and the available solutions are also too complex to be found. The perfect blend made of bioinformatics and neural networks technique results in efficient DNA pattern analysis algorithm with utmost prediction accuracy.