在医院内提供生物信息学支持的十条简单规则。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Davide Chicco, Giuseppe Jurman
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引用次数: 0

摘要

生物信息学已成为许多医院科学中心生物医学研究计划的一个重要方面,在医院内建立生物信息学设施已成为全世界的普遍做法。在这些机构工作的生物信息学家为医生和主要研究人员提供计算生物学方面的支持,他们每天都要分析病人的数据。这些生物信息分析师虽然举足轻重,但通常没有接受过正规的培训。因此,我们提出了这十条简单的规则来指导这些生物信息学家的工作:关于如何为医院医生提供生物信息学支持的十条建议。我们相信,这些简单的规则可以帮助生物信息学设施分析人员取得更好的科研成果,并在一个宁静而富有成效的环境中工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ten simple rules for providing bioinformatics support within a hospital.

Bioinformatics has become a key aspect of the biomedical research programmes of many hospitals' scientific centres, and the establishment of bioinformatics facilities within hospitals has become a common practice worldwide. Bioinformaticians working in these facilities provide computational biology support to medical doctors and principal investigators who are daily dealing with data of patients to analyze. These bioinformatics analysts, although pivotal, usually do not receive formal training for this job. We therefore propose these ten simple rules to guide these bioinformaticians in their work: ten pieces of advice on how to provide bioinformatics support to medical doctors in hospitals. We believe these simple rules can help bioinformatics facility analysts in producing better scientific results and work in a serene and fruitful environment.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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