Journal•ISSN: 1744-5485
International Journal of Bioinformatics Research and Applications
Inderscience Publishers
About: International Journal of Bioinformatics Research and Applications is an academic journal published by Inderscience Publishers. The journal publishes majorly in the area(s): Computer science & Biology. It has an ISSN identifier of 1744-5485. Over the lifetime, 487 publications have been published receiving 3047 citations. The journal is also known as: Bioinformatics research and application & IJBRA.
Papers published on a yearly basis
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TL;DR: The (my)Grid ontology is one component in a larger semantic discovery framework for the identification of the highly distributed and heterogeneous bioinformatics services in the public domain and adopt a spectrum of expressivity and reasoning for different tasks in service annotation and discovery.
Abstract: myGrid supports in silico experiments in the life sciences, enabling the design and enactment of workflows as well as providing components to assist service discovery, data and metadata management. The myGrid ontology is one component in a larger semantic discovery framework for the identification of the highly distributed and heterogeneous bioinformatics services in the public domain. From an initial model of formal OWL-DL semantics throughout, we now adopt a spectrum of expressivity and reasoning for different tasks in service annotation and discovery. Here, we discuss the development and use of the myGrid ontology and our experiences in semantic service discovery.
96 citations
TL;DR: A novel model-based compression technique is proposed that makes use of clinically relevant regions as defined by radiologists, and is proposed to maximise compression while maintaining clinical relevance and balancing legal risk.
Abstract: As medical/biological imaging facilities move towards complete film-less imaging, compression plays a key role. Although lossy compression techniques yield high compression rates, the medical community has been reluctant to adopt these methods, largely for legal reasons, and has instead relied on lossless compression techniques that yield low compression rates. The true goal is to maximise compression while maintaining clinical relevance and balancing legal risk. This paper proposes a novel model-based compression technique that makes use of clinically relevant regions as defined by radiologists. Lossless compression is used in these clinically relevant regions, and lossy compression is used everywhere else.
66 citations
TL;DR: In this paper, an attempt to review the evolution of MEGA software, working and application has been made and data analysis, implementation and advantages over other bioinformatics software have been discussed systematically.
Abstract: Biocomputing has moved into central position in molecular biology research. Enormous improvements in genetic engineering have led to the accumulation of a vast amount of biological information. With the advent of this extensive repertoire of raw sequence information, the next major challenge for a modern researcher is to interpret this biological information. Molecular Evolutionary Genetic Analysis (MEGA) is bio-computational software to fill the vacuum between data development and analysis. In this paper, an attempt to review the evolution of MEGA software, working and application has been made. Moreover, data analysis, implementation and advantages over other bioinformatics software have been discussed systematically.
55 citations
TL;DR: This study focuses on the identification of Motor Imagery tasks for the development of Brain Computer Interface technologies combining Cross-Correlation and Logistic Regression techniques, and the proposed method results in an improvement of at least 3.47% compared with the existing methods tested.
Abstract: This study focuses on the identification of Motor Imagery MI tasks for the development of Brain Computer Interface BCI technologies combining Cross-Correlation and Logistic Regression CC-LR techniques. The proposed method is tested on two benchmark data sets, IVa and IVb of BCI Competition III, and the performance is evaluated through a 3-fold cross-validation procedure. The experimental outcomes are compared with two recently reported algorithms, R-Common Spatial Pattern CSP with aggregation and Clustering Technique CT-based Least Square Support Vector Machine LS-SVM and also other four algorithms using data set IVa. The results demonstrate that our proposed method results in an improvement of at least 3.47% compared with the existing methods tested.
53 citations
TL;DR: A new method called Complete Composition Vector, which is a collection of Composition Vectors (CV), is described to infer evolutionary relationships between species using their complete genomic sequences, which bypasses the complexity of performing multiple sequence alignments and avoids the ambiguity of choosing individual genes for species tree construction.
Abstract: A new method called Complete Composition Vector (CCV), which is a collection of Composition Vectors (CV), is described to infer evolutionary relationships between species using their complete genomic sequences. Such a method bypasses the complexity of performing multiple sequence alignments and avoids the ambiguity of choosing individual genes for species tree construction. It is expected to effectively retain the rich evolutionary information contained in the whole genomic sequence. The method was applied to infer the evolutionary footprints for several datasets that have been previously studied. The final phylogenies were built by an improved clustered Neighbour-Joining method. The generated phylogenetic trees are highly consistent with taxonomy hierarchy and previous studies, with some biologically interesting disagreements.
53 citations