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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bioinformatics-your-journey-of-becoming-bioinformatician/
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课程名称：生物信息学：成为生物信息学家的旅程 课程概述：生物信息学作为一个跨学科领域，起源于生物医学研究中对计算解决方案的需求。随着80年代计算技术的便宜和普及，90年代互联网的发展，以及2000年代高通量技术的日益普及，这一领域不断演变。Ranganathan指出，生物信息学与生物医学学科之间的界限已变得模糊，近年来生物信息学及其子学科（如化学信息学、神经信息学和免疫信息学）不断涌现。生物信息学相关子学科的数量似乎仅受限于学科本身的数量。这是因为对于生物问题的综合性视角需求日益增长，因此这种跨学科的努力在科学进步中显得愈发重要。商业、政府和教育机构需要在生物信息学资源分配、培训和人才教育方面，基于各自的学科努力做出长期和短期的战术决策。 本课程旨在警示学生生物信息学教育和培训的价值，帮助教育工作者认识到生物信息学对其领域的影响，同时支持管理者和行政人员更好地规划计算资源的分配。尽管生物信息学研究中的每一个活跃领域可能会有不同长度的“半衰期”，但这一分析将为各方提供重要的指导。
Bioinformatics is originated as cross-disciplinary field and it is the need for computational solutions to research problems raised in Biomedicine. This field is evolved as computation that became cheaper and widespread during the 80s, as the Internet grew during the 90s, and as high output technologies become more common in the 2000, s. The Ranganathan noted boundaries between bioinformatics and biomedical disciplines have become distorted and indeed, recent years have seen spawning of the bioinformatics and sub-chastisements which are cheminformatics, neuroinformatic and immunoinformatic. Apparently, the only limit to the number of bioinformatics related sub-chastisements are the numbers of disciplines themselves. This is because of growing need for a consolidative view of biological problems, cross disciplinary efforts such as these are considered increasingly important to continued scientific progress. Subsequently, commercial, government and educational institutes need to make both long-term and short-term tactical decisions about bioinformatics-based resource allocation, training and workforce education based upon their disciplinal efforts.Even though every active area of bioinformatics research interest will likely have a ‘half-life' of varying length, this analysis should benefit students by alerting them to the benefit of bioinformatics education and/or training, educators to become aware of the effects of bioinformatics in their fields, and for managers/administrators to better plan computational resource allocations.
