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Molecular Omics
基本信息
期刊名称 Molecular Omics
MOL OMICS
期刊ISSN 2515-4184
期刊官方网站 https://pubs.rsc.org/en/journals/journalissues/mo
是否OA No
出版商 Royal Society of Chemistry
出版周期 Bi-monthly
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始发年份 2005
年文章数 58
影响因子 3.0(2023)  scijournal影响因子  greensci影响因子
中科院SCI期刊分区
大类学科 小类学科 Top 综述
CiteScore
CiteScore排名 CiteScore SJR SNIP
学科 排名 百分位 5.4 0.761 0.576
Biochemistry, Genetics and Molecular Biology
Genetics
151/347 56%
Biochemistry, Genetics and Molecular Biology
Biochemistry
225/438 48%
Biochemistry, Genetics and Molecular Biology
Molecular Biology
236/410 42%
补充信息
自引率 3.3%
H-index 70
SCI收录状况 Science Citation Index Expanded
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PubMed Central (PMC) http://www.ncbi.nlm.nih.gov/nlmcatalog?term=2515-4184%5BISSN%5D
投稿指南
期刊投稿网址 https://mc.manuscriptcentral.com/mo
收稿范围
Molecular Omics publishes high-quality research from across the -omics sciences that provide significant new insight into important chemical or biological problems.

Molecular Omics articles report research that significantly increases understanding or demonstrates clear functional benefits, supported by experimental validation or a novel data analytic approach.
Topics include, but are not limited to:

• omics studies to gain mechanistic insight into biological processes – for example, determining the mode of action of a drug or the basis of a particular phenotype, such as drought tolerance.

• omics studies for clinical applications with experimental validation, such as finding biomarkers for diagnostics or potential new drug targets.

• omics studies looking at the sub-cellular make-up of cells – for example, the subcellular localisation of certain proteins or post-translational modifications or new imaging techniques.

• studies presenting new methods and tools to support omics studies, including new spectroscopic/chromatographic techniques, chip-based/array technologies and new classification/data analysis techniques. New methods should be proven and demonstrate an advance in the field.

The following are not within the scope of Molecular Omics:

• Research based on reanalysis of public datasets without experimental validation and significant omics datasets.
• Papers without significant biological justification or interpretation.
• Research that is only of interest to the specialist in the area.
• Papers reporting new results that could be routinely predicted.
• Papers that do not show a significant improvement over known research.
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