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

Publisher: Inderscience

Journal description

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.

Current impact factor: 0.00

Impact Factor Rankings

Additional details

5-year impact 0.00
Cited half-life 0.00
Immediacy index 0.00
Eigenfactor 0.00
Article influence 0.00
Website International Journal of Bioinformatics Research and Applications website
Other titles Bioinformatics research and applicatons, IJBRA
ISSN 1744-5485
OCLC 300961439
Material type Periodical, Internet resource
Document type Internet Resource, Journal / Magazine / Newspaper

Publisher details

Inderscience

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author cannot archive a post-print version
  • Restrictions
    • 6 months embargo
  • Conditions
    • Cannot archive until publication
    • Author's pre-print and Author's post-print on author's personal website, institutional repository or subject repository
    • Publisher copyright and source must be acknowledged
    • Must link to journal webpage and /or DOI
    • Publisher's version/PDF cannot be used, unless covered by funding agency rules
    • Authors covered by funding agency rules, may post the Publisher's Version/PDF in subject repositories after a 6 months embargo
    • Reviewed 10/02/2014
    • Author's post-print equates to Inderscience's Proof
  • Classification
    ‚Äč yellow

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: BioInt, a biological programming application framework and interpreter, is an attempt to equip the researchers with seamless integration, efficient extraction and effortless analysis of the data from various biological databases and algorithms. Based on the type of biological data, algorithms and related functionalities, a biology-specific framework was developed which has nine modules. The modules are a compilation of numerous reusable BioADTs. This software ecosystem containing more than 450 biological objects underneath the interpreter makes it flexible, integrative and comprehensive. Similar to Python, BioInt eliminates the compilation and linking steps cutting the time significantly. The researcher can write the scripts using available BioADTs (following C++ syntax) and execute them interactively or use as a command line application. It has features that enable automation, extension of the framework with new/external BioADTs/libraries and deployment of complex work flows.
    International Journal of Bioinformatics Research and Applications 05/2015; 11(3):247-256. DOI:10.1504/IJBRA.2015.069195
  • International Journal of Bioinformatics Research and Applications 01/2015; 11(3):268. DOI:10.1504/IJBRA.2015.069225
  • International Journal of Bioinformatics Research and Applications 01/2015; 11(3):200. DOI:10.1504/IJBRA.2015.069186
  • International Journal of Bioinformatics Research and Applications 01/2015; 11(3):219. DOI:10.1504/IJBRA.2015.069193
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    ABSTRACT: The incidence of bacterial disease has increased tremendously in the last decade, because of the emergence of drug resistance strains within the bacterial pathogens. The present study was to investigate the antibacterial compound 2,5-di-tert-butyl-1,4-benzoquinone (DTBBQ) isolated from marine Streptomyces sp. VITVSK1 as a potent antibacterial agent. The antibacterial potential of DTBBQ was investigated against RNA Polymerase (PDB ID-1I6V) by in silico molecular docking tools. Results of our study showed the high affinity interaction between DTBBQ and RNA polymerase and also confirmed the drug likeliness of DTBBQ using ADMET in silico pharmacology tools. Our findings suggest that DTBBQ could be used as antibacterial drug to defend the emerging antibacterial resistance.
    International Journal of Bioinformatics Research and Applications 01/2015; 11(2):142-52. DOI:10.1504/IJBRA.2015.068089
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    ABSTRACT: The human gut is one of the most densely populated microbial communities in the world. The interaction of microbes with human host cells is responsible for several disease conditions and of criticality to human health. It is imperative to understand the relationships between these microbial communities within the human gut and their roles in disease. In this study we analyse the microbial communities within the human gut and their role in Inflammatory Bowel Disease (IBD). The bacterial communities were interrogated using Length Heterogeneity PCR (LH-PCR) fingerprinting of mucosal and luminal associated microbial communities for a class of healthy and diseases patients.
    International Journal of Bioinformatics Research and Applications 01/2015; 11(2):111-29. DOI:10.1504/IJBRA.2015.068087
  • International Journal of Bioinformatics Research and Applications 01/2015; 11(3):187. DOI:10.1504/IJBRA.2015.069185
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    ABSTRACT: Investigation of essential proteins is significantly valuable for understanding of cellular life, drug design and other practical purposes. In most of current studies, essential proteins are generally mined in protein-protein interaction (PPI) networks with diverse topology features. In this study, we investigate what kind of proteins is inclined to be essential from a new perspective. The investigation implies that protein essentiality is correlated with protein domains, which are functional, structural and evolutionary units of proteins. Proteins with a larger Number of Domain Types (NDT) tend to be essential. The analyses on 22 species show that essential proteins identified by NDT are much more than those identified by ten random identifications. The consideration of the structural feature makes us less dependent on network data and thus enables us to investigate protein essentiality of more species with incomplete and/or inconsistent network data.
    International Journal of Bioinformatics Research and Applications 01/2015; 11(2):91-110. DOI:10.1504/IJBRA.2015.068086
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    ABSTRACT: Hepatitis Delta Virus (HDV) is an RNA virus and causes delta hepatitis in humans. Although a lot of data is available for HDV, but retrieval of information is a complicated task. Current web database 'HDVDB' provides a comprehensive web-resource for HDV. The database is basically concerned with basic information about HDV and disease caused by this virus, genome structure, pathogenesis, epidemiology, symptoms and prevention, etc. Database also supplies sequence data and bibliographic information about HDV. A tool 'siHDV Predict' to design the effective siRNA molecule to control the activity of HDV, is also integrated in database. It is a user friendly information system available at public domain and provides annotated information about HDV for research scholars, scientists, pharma industry people for further study.
    International Journal of Bioinformatics Research and Applications 01/2015; 11(2):162-70. DOI:10.1504/IJBRA.2015.068091
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    ABSTRACT: In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.
    International Journal of Bioinformatics Research and Applications 05/2014; 11(2). DOI:10.1504/IJBRA.2015.068092
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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. DOI:10.1504/IJBRA.2014.059521
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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. DOI:10.1504/IJBRA.2014.059518
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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. DOI:10.1504/IJBRA.2014.058775
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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. DOI:10.1504/IJBRA.2014.059519