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Bioinformatics is an emerging discipline where the interdisciplinary research holds great promise for the advancement of research and development in many complex areas. The research output generates a huge amount of data and information. Because of the heterogeneous nature of bioinformatics resources, difficulty in accessing pertinent information is the biggest challenge for the bioinformatics community. The integration of bioinformatics resources in a comprehensive manner is advocated by the bioinformatics user community as well as by information scientists serving this community. There are have already been some efforts made for integration of bioinformatics resources by the discrete bioinformatics community, but these are based on the requirement of their own area and arena. This paper aims to discuss the design and development of a tool for the integration of various heterogeneous bioinformatics information resources available over the internet.

The authors have developed a tool with the acronym “iBIRA” (Integrated Bioinformatics Information Resource Access) that associates the bioinformatics community with the bioinformatics “resourceome” (the term suggested for the “full set of bioinformatics resources” by Cannata et al.). Available over the internet. iBIRA (www.ibiranet.in) integrates bioinformatics resources in a way such that it is possible to locate, connect and communicate different categories of resources in a cohesive manner. A software engineering and database‐driven approach was used for the integration and organization of bioinformatics resources. Computational programming such as Hypertext Preprocessor (PHP), a server‐side dynamic web programming language, and MySQL as a database management system have been used. Dublin Core Metadata Standards have been used for the design of metadata for bioinformatics resources..

The term “resource” in the area of bioinformatics covers various entities such as journals, molecular biology databases, online annotation tools, patents, published documents (articles, books, etc), protocols, software tools, and web servers. It has been found that bioinformatics resources are heterogeneous in nature and available over the internet in different forms and formats. The fact that bioinformatics resources are scattered over the internet makes resource discovery difficult for the bioinformatics community, and there is need for a system that reorganizes these resources. The integration of all the resources of bioinformatics at a single platform (called “iBIRA”) provides significant “value added” to the bioinformatics community, those serving this population.

The iBIRA tool is a meta‐server developed to provide information service about the availability of various bioinformatics resources to the bioinformatics community. This will provide a value‐added benefit to the population in helping them to locate relevant resources for their education, research and training.

Bioinformatics projects are currently under way at numerous universities and in industry. These projects typically involve processing large amounts of biological data and comparison of biological signals or sequences. Much of the existing work in bioinformatics software is based on such languages and platforms as Perl and Unix. This paper, proposes software architectures in Java to support biological applications allowing access of biological data using server‐side Java programs on the Internet. The architecture follows the standards of unified modeling language (UML). UML architecture diagrams are presented for the Java‐based bioinformatics applications. In addition, an overview of the Bio‐Soft project under way at The Biomedical Research Institute (BRI) of the University of Wisconsin‐Parkside is provided, which includes research and instructional software for bioinformatics applications.

In the Fall semester of 2001, a new position – Bioinformatics Librarian – was developed jointly by the University of Florida's Health Science Center Libraries (HSCL) and Genetics Institute (UFGI). Aims to give an overview of this post.

This paper describes the development of the position and the services provided.

Funded by the Genetics Institute and housed in the library, this position was created to meet the information needs of the university's faculty, students and staff involved in genetics and bioinformatics research and study. The responsibilities of the position were in part patterned after those performed via the HSCL's existing Liaison Librarian program. Librarians with only an undergraduate degree in the biosciences can still make an important, albeit usually less complete, contribution in this area.

The University of Florida's Bioinformatics Librarian position may serve as a model for the Information Specialist in Context (ISIC; Informationist; Bioinformationist) in the research arena.

This paper aims to supply an introduction to the bioinformatics discipline for information professionals, outlining how current information management issues are hampering the effective integration and interoperability of resources.

The approach taken is to outline some of the more challenging issues to illustrate their consequences, such as syntactic and semantic heterogeneity, data storage formats and media, and the existence of inconsistencies in information content in bioinformatics resources. A discussion of these topics indicates how semantic web concepts and technologies, together with e‐science initiatives, can be used to address these problems.

The paper reveals that, if one considers the use of semantic web technologies such as XML and ontologies for the development of information standards that allows integration of different information systems, these systems could then be placed into applications such as web services and GRIDS tailored for biological studies. Such applications would provide automated functionality for database integration, workflow management, inclusion of provenance data, and notification of services.

The value of this paper is that it exemplifies how information professionals can make an impact on the discipline of bioinformatics, which historically has not been addressed by experts with information skills.

A review was carried out of the ‘information landscape’ within the pharmaceuticals‐based molecular biology community, which examined the research problems requiring biological‐sequence data, important sources of information, methods of access, information‐seeking behaviour of end users and the role of libraries and information centres. This work concentrated on the practical aspects of how biological sequence information is managed and used in a research setting and was carried out as part of the MSc in Information Science at the City University. Fifteen questionnaires were sent to information scientists in the UK pharmaceutical industry and a user study was carried out amongst scientists at Celltech. Most of the important primary data are available freely or cheaply via the Internet and molecular biologists were found to be self‐reliant in their use of these resources. Currency of information was found to be very important in the research process and the issue of Internet security was taken very seriously. Most questionnaire respondents saw a productive role in the future for information workers in the field of molecular biology, citing end‐user training and data integration as possible roles, although the degree of involvement will depend on the particular mix of skills and experience that exist within an information department.

To present research in the area of the applications of modern heuristics and data mining techniques in knowledge discovery.

Applications of data mining for neural networks using NeuralWare Predict^® software, genetic algorithms using Biodiscovery GeneSight^® (2005) software, and regression and discriminant analysis using SPSS^® were selected for bioscience data sets of continuous numerical‐valued Abalone fish data and discrete nominal‐valued mushroom data.

This paper illustrates the useful information that can be obtained using data mining for evolutionary algorithms specifically as those for neural networks, genetic algorithms, regression analysis, and discriminant analysis.

The use of NeuralWare Predict^® was a very effective method of implementing training rules for neural networks to identify the important attributes of numerical and nominal valued data.

The software and algorithms discussed in the paper can be used to visualize and mine microarray data.

The paper contributes to the discussion on the data visualization and data mining of microarray database for bioinformatics and emphasizes new applicability of modern heuristics and software.

The purpose of this paper is to present the design and implementation of a blended problem-based learning (PBL) teaching model and corresponding teaching materials for a university-level bioinformatics course. The effects of this teaching model on student performance in terms of problem solving and learning attitudes were investigated.

Students in both groups completed assessments of problem-solving attitudes and learning attitudes both one week both before and after experimental instruction. Collected data were analyzed using one-way ANCOVA. Group discussions and student interviews were recorded and treated as part of the data analysis.

The study produced the following findings: the experimental group was found to perform better than the control group in terms of learning attitudes, but the results were not statistically significant; the experimental group was found to outperform the control group in terms of problem-solving attitudes, and the difference was statistically significant; lesson plan contents need to be designed based on problem-based learning theory, and reflect real-world conditions; participants in the experimental group approved of the blended PBL and group discussion approach.

Results are expected to provide a useful reference for educators and researchers. These findings can be applied to relevant instructional fields to enhance learner motivation and engagement, thus improving learning outcomes.

The problem of motif discovery has become a significant challenge in the era of big data where there are hundreds of genomes requiring annotations. The importance of motifs has led many researchers to develop different tools and algorithms for finding them. The purpose of this paper is to propose a new algorithm to increase the speed and accuracy of the motif discovering process, which is the main drawback of motif discovery algorithms.

All motifs are sorted in a tree-based indexing structure where each motif is created from a combination of nucleotides: ‘A’, ‘C’, ‘T’ and ‘G’. The full motif can be discovered by extending the search around 4-mer nucleotides in both directions, left and right. Resultant motifs would be identical or degenerated with various lengths.

The developed implementation discovers conserved string motifs in DNA without having prior information about the motifs. Even for a large data set that contains millions of nucleotides and thousands of very long sequences, the entire process is completed in a few seconds.

Experimental results demonstrate the efficiency of the proposed implementation; as for a real-sequence of 1,270,000 nucleotides spread into 2,000 samples, it takes 5.9 s to complete the overall discovering process when the code ran on an Intel Core i7-6700 @ 3.4 GHz machine and 26.7 s when running on an Intel Xeon x5670 @ 2.93 GHz machine. In addition, the authors have improved computational performance by parallelizing the implementation to run on multi-core machines using the OpenMP framework. The speedup achieved by parallelizing the implementation is scalable and proportional to the number of processors with a high efficiency that is close to 100%.

The purpose of this paper is to analyse the global scientific outputs of ontology research, an important emerging discipline that has huge potential to improve information understanding, organization, and management.

This study collected literature published during 1900-2012 from the Web of Science database. The bibliometric analysis was performed from authorial, institutional, national, spatiotemporal, and topical aspects. Basic statistical analysis, visualization of geographic distribution, co-word analysis, and a new index were applied to the selected data.

Characteristics of publication outputs suggested that ontology research has entered into the soaring stage, along with increased participation and collaboration. The authors identified the leading authors, institutions, nations, and articles in ontology research. Authors were more from North America, Europe, and East Asia. The USA took the lead, while China grew fastest. Four major categories of frequently used keywords were identified: applications in Semantic Web, applications in bioinformatics, philosophy theories, and common supporting technology. Semantic Web research played a core role, and gene ontology study was well-developed. The study focus of ontology has shifted from philosophy to information science.

This is the first study to quantify global research patterns and trends in ontology, which might provide a potential guide for the future research. The new index provides an alternative way to evaluate the multidisciplinary influence of researchers.