Educating the educators: Incorporating bioinformatics into biological science education in Malaysia

Bioinformatics can be defined as a fusion of computational and biological sciences. The urgency to process and analyse the deluge of data created by proteomics and genomics studies has caused bioinformatics to gain prominence and importance. However, its multidisciplinary nature has created a unique demand for specialist trained in both biology and computing. In this review, we described the components that constitute the bioinformatics field and distinctive education criteria that are required to produce individuals with bioinformatics training. This paper will also provide an introduction and overview of bioinformatics in Malaysia. The existing bioinformatics scenario in Malaysia was surveyed to gauge its advancement and to plan for future bioinformatics education strategies. For comparison, we surveyed methods and strategies used in education by other countries so that lessons can be learnt to further improve the implementation of bioinformatics in Malaysia. It is believed that accurate and sufficient steerage from the academia and industry will enable Malaysia to produce quality bioinformaticians in the future

Sticks, balls or a ribbon? Results of a formative user study with bioinformaticians

User interfaces in modern bioinformatics tools are designed for experts. They are too complicated for\ud novice users such as bench biologists. This report presents the full results of a formative user study as part of a\ud domain and requirements analysis to enhance user interfaces and collaborative environments for\ud multidisciplinary teamwork. Contextual field observations, questionnaires and interviews with bioinformatics\ud researchers of different levels of expertise and various backgrounds were performed in order to gain insight into\ud their needs and working practices. The analysed results are presented as a user profile description and user\ud requirements for designing user interfaces that support the collaboration of multidisciplinary research teams in\ud scientific collaborative environments. Although the number of participants limits the generalisability of the\ud findings, the combination of recurrent observations with other user analysis techniques in real-life settings\ud makes the contribution of this user study novel

Applying a User-centred Approach to Interactive Visualization Design

Analysing users in their context of work and finding out how and why they use different information resources is essential to provide interactive visualisation systems that match their goals and needs. Designers should actively involve the intended users throughout the whole process. This chapter presents a user-centered approach for the design of interactive visualisation systems. We describe three phases of the iterative visualisation design process: the early envisioning phase, the global specification hase, and the detailed specification phase. The whole design cycle is repeated until some criterion of success is reached. We discuss different techniques for the analysis of users, their tasks and domain. Subsequently, the design of prototypes and evaluation methods in visualisation practice are presented. Finally, we discuss the practical challenges in design and evaluation of collaborative visualisation environments. Our own case studies and those of others are used throughout the whole chapter to illustrate various approaches

Bioinformatics and the politics of innovation in the life sciences: Science and the state in the United Kingdom, China, and India

The governments of China, India, and the United Kingdom are unanimous in their belief that bioinformatics should supply the link between basic life sciences research and its translation into health benefits for the population and the economy. Yet at the same time, as ambitious states vying for position in the future global bioeconomy they differ considerably in the strategies adopted in pursuit of this goal. At the heart of these differences lies the interaction between epistemic change within the scientific community itself and the apparatus of the state. Drawing on desk-based research and thirty-two interviews with scientists and policy makers in the three countries, this article analyzes the politics that shape this interaction. From this analysis emerges an understanding of the variable capacities of different kinds of states and political systems to work with science in harnessing the potential of new epistemic territories in global life sciences innovation

Are We Legislating Away Our Scientific Future? The Database Debate

The ambiguity of the present copyright laws governing the protection of databases creates a situation where database owners, unsure of how IP laws safeguard their information, overprotect their data with oppressive licenses and technological mechanisms (condoned by the DMCA) that impede interoperation. Databases are fundamental to scientific research, yet the lack of interoperability between databases and limited access inhibits this research. The US Congress, spurred by the European Database Directive, and heavily lobbied by the commercial database industry, is presently considering ways to legislate database protections; most of the present suggestions for legislation will be detrimental to scientific progress. The author agrees that new legislation is necessary, but not to provide extra-copyright protections, as database owners would like, but to create an environment wherein data is easily accessible to academic research and interoperability is encouraged; yet simultaneously providing database owners with incentives to produce new databases. One possibility would be to introduce standardized compulsory licensing of databases to academics following an embargo period where databases could be sold at free-market prices (to recoup costs). Databases would be given some sort of intellectual property protection both during and after this embargo in return for a limiting of technical safeguards and conforming to interoperability standards

A Quick Guide for Computer-Assisted Instruction in Computational Biology and Bioinformatics

Introduction: Computational Biology and Bioinformatics (CBB) are indispensable components in the training of life scientists [1–3]. Current curricula in the life sciences should prepare graduates who master quantitative and computer skills for increased levels of performance [4–6]. Equally important is that the application of the curricula is driven by an appropriate instructional paradigm and effective learning experiences. Teaching and learning with computers bring specific issues that should be considered beforehand by any instructor. The following Quick Guide for Computer-Assisted Instruction (CAI) outlines ten principles for effective teaching. The principles are aligned with current developments on human cognition and learning [7] and have been drawn from our own experience using CAI in seminars, tutorials, and distance education, in courses on Molecular Life Sciences at the undergraduate level, taught to majors in biology or in other subjects (e.g., nutrition, teaching of physics and chemistry, teaching of biology, sports). The Guide refers to the preparation, presentation, and assessment of CAI. It should be an aid for those who teach CBB with CAI in class, and it is expected to stimulate student motivation and deeper learning in CBB, thus making class time more effective and improving satisfaction of both students and instructors

Agents in Bioinformatics

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions