International Journal of Bioinformatics Research and Applications (Int J Bioinformatics Res Appl )


Bioinformatics is a new scientific discipline that combines biology, computer science, mathematics, and statistics into a broad-based field that will have profound impacts on all fields of biology. Bioinformatics is expected to substantially impact on scientific, engineering and economic development of the world. Research and development in bioinformatics and computational biology require the cooperation of specialists from the fields of biology, computer science, mathematics, statistics, physics, and such related sciences. It is the comprehensive application of mathematics (e.g., probability and graph theory), statistics, science (e.g., biochemistry), and computer science (e.g., computer algorithms and machine learning) to the understanding of living systems. Bioinformatics is fast emerging as an important discipline for academic research and industrial application. The large size of biological data sets, inherent complexity of biological problems and the ability to deal with error-prone data all result in special requirements such as large memory space and huge computation time. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications.

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    International Journal of Bioinformatics Research and Applications website
  • Other titles
    Bioinformatics research and applicatons, IJBRA
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  • Material type
    Periodical, Internet resource
  • Document type
    Internet Resource, Journal / Magazine / Newspaper

Publications in this journal

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    ABSTRACT: A key challenge in upper extremity neuroprosthetics is variable levels of skill and inconsistent functional recovery. We examine the feasibility and benefits of using natural neuromotor strategies through the design and development of a proof-of-concept model for a feed-forward upper extremity neuroprosthetic controller. Developed using Artificial Neural Networks, the model is able to extract and classify neural correlates of movement intention from multiple brain regions that correspond to functional movements. This is unique compared to contemporary controllers that record from limited physiological sources or require learning of new strategies. Functional MRI (fMRI) data from healthy subjects (N = 13) were used to develop the model, and a separate group (N = 4) of subjects were used for validation. Results indicate that the model is able to accurately (81%) predict hand movement strictly from the neural correlates of movement intention. Information from this study is applicable to the development of upper extremity technology aided interventions.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):217-34.
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    ABSTRACT: Mutual information, conditional mutual information and interaction information have been widely used in scientific literature as measures of dependence, conditional dependence and mutual dependence. However, these concepts suffer from several computational issues; they are difficult to estimate in continuous domain, the existing regularised estimators are almost always defined only for real or vector-valued random variables, and these measures address what dependence, conditional dependence and mutual dependence imply in terms of the random variables but not finite realisations. In this paper, we address the issue that given a set of realisations in an arbitrary metric space, what characteristic makes them dependent, conditionally dependent or mutually dependent. With this novel understanding, we develop new estimators of association, conditional association and interaction association. Some attractive properties of these estimators are that they do not require choosing free parameter(s), they are computationally simpler, and they can be applied to arbitrary metric spaces.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):43-58.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):206-16.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(3):264-96.
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    ABSTRACT: Transposable Elements (TEs) play important roles in the evolution of eukaryotic organisms. TEs widely distribute depending on their properties present in the genome. This study elucidated the molecular characteristics of TEs in land plants and animals using bioinformatics and in silico mutational approach. We discovered that the GC-rich class I TEs is the predominant class of TEs in animal, but the AT-rich class II TEs is prevalent in plants. The GC-rich class I TEs appears to be evolved within the animals. On contrary, the preserved in AT-rich in class II TEs is believed to be contributed in host defence systems.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(3):297-306.
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    ABSTRACT: Identification of targets, generally viruses or bacteria, in a biological sample is a relevant problem in medicine. Biologists can use hybridisation experiments to determine whether a specific DNA fragment, that represents the virus, is presented in a DNA solution. A probe is a segment of DNA or RNA, labelled with a radioactive isotope, dye or enzyme, used to find a specific target sequence on a DNA molecule by hybridisation. Selecting unique probes through hybridisation experiments is a difficult task, especially when targets have a high degree of similarity, for instance in a case of closely related viruses. After preliminary experiments, performed by a canonical Monte Carlo method with Heuristic Reduction (MCHR), a new combinatorial optimisation approach, the Space Pruning Monotonic Search (SPMS) method, is introduced. The experiments show that SPMS provides high quality solutions and outperforms the current state-of-the-art algorithms.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):59-74.
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    ABSTRACT: Finding the secondary structures of ribonucleic acid sequences is a very important task. The secondary structure helps determine their functionalities which in turn plays a role in the proteins production. Manual laboratory methods use X-ray diffraction to predict secondary structures but it is complex, slow and expensive. Therefore, different computational approaches are used to predict RNA secondary structure in order to reduce the time and cost associated with the manual process. We propose a system called IsRNA to predict a single element, internal loop, of the RNA secondary structure. IsRNA experiments with different classifiers such as SVM, KNN, Naive Bayes and Simple K means to find the most accurate classifier. We present a through experimental evaluation of 24 features, classified into five groups, to determine the most relevant feature groups. The system is evaluated using Rfam sequences and achieves an overall sensitivity, selectivity, and accuracy of 96.1%, 98%, and 96.1%, respectively.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(3):307-20.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):157-76.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(3):321-40.
  • International Journal of Bioinformatics Research and Applications 01/2014;
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    ABSTRACT: β-lactam antibiotics are utilised to treat bacterial infection. β-lactamase enzymes (EC are produced by several bacteria and are responsible for their resistance to β-lactam antibiotics like penicillin, cephamycins and carbapenems. New Delhi Metallo-β-lactamase (NDM-1) is a gene that makes bacteria resistant to β-lactam antibiotics. Preparing a compound against NDM-1 will require additional investment and development by drug manufacturers as the current antibiotics will not treat patients with NDM-1 resistance. NDM-1 of Kolkata showed convergent-type evolution with other NDM-1 producing strains. The modelled structure exhibited α-β-α barrel-type domain along with Zn metallo-β-lactamase N-terminal domain. Compounds belonging to cephalosporins (relatively resistant to β-lactamase) and other antibiotics ceftaroline, ceftobiprole, piperacillin, penamecillin, azidocillin, cefonicid, tigecycline and colistin have exhibited better binding affinity with the modelled NDM-1.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(3):235-63.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):93-109.
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    ABSTRACT: Mass spectrometry (MS) has become the method of choice to study the proteome of brain injury. The high throughput nature of MS-based proteomic experiments generates massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. In this review, a brief overview of the currently available bioinformatics strategies applied to neuroproteomic studies is presented. Application of commercially available bioinformatics software to different brain injury studies demonstrates integration of the data mining and analysis applications into neuroproteomic workflows that can identify major protein markers as well as highlight the biological processes and molecular functions involved.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):27-42.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):190-205.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):4-26.
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    ABSTRACT: Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):129-44.
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    ABSTRACT: We develop a general method for computing extreme value distribution (Gumbel, 1958) parameters for gapped alignments. Our approach uses mixture distribution theory to obtain associated BLOSUM matrices for gapped alignments, which in turn are used for determining significance of gapped alignment scores for pairs of biological sequences. We compare our results with parameters already obtained in the literature.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):177-89.
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    ABSTRACT: 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.
    International Journal of Bioinformatics Research and Applications 01/2014; 10(1):75-92.