کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8408569 | 1545070 | 2017 | 37 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges
ترجمه فارسی عنوان
سیستم های محاسباتی برای تجزیه و تحلیل داده های زیستی بزرگ: چشم اندازها و چالش ها
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
بیوتکنولوژی یا زیستفناوری
چکیده انگلیسی
The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational and Structural Biotechnology Journal - Volume 15, 2017, Pages 403-411
Journal: Computational and Structural Biotechnology Journal - Volume 15, 2017, Pages 403-411
نویسندگان
Zekun Yin, Haidong Lan, Guangming Tan, Mian Lu, Athanasios V. Vasilakos, Weiguo Liu,