生物信息学:前景与进展

Q4 Health Professions International Journal of Bioinformatics Research and Applications Pub Date : 2015-09-01 DOI:10.1504/IJBRA.2015.071945

Shipra Gupta, G. Misra, S. Khurana

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引用次数: 3

摘要

生物信息学是一门解决和分析生物问题的多学科科学。随着生物医学数据的量子爆炸，生物信息学的需求逐渐增加。本文概述了神经学、结构与功能生物学、进化生物学、临床科学等领域的生物学家或生物研究人员利用生物信息学应用进行数据分析的各种方法，以总结他们的研究。一个新的角度被用来分类的知识，在该领域，从而将有助于一般观众了解生物信息学的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics: promises and progress

Bioinformatics is a multidisciplinary science that solves and analyzes biological problems. With the quantum explosion in biomedical data, the demand of bioinformatics has increased gradually. Present paper provides an overview of various ways through which the biologists or biological researchers in the domain of neurology, structural and functional biology, evolutionary biology, clinical science, etc., use bioinformatics applications for data analysis to summarise their research. A new perspective is used to classify the knowledge available in the field thus will help general audience to understand the application of bioinformatics.

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.