Example of Current Bioinformatics format
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Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format
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Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format Example of Current Bioinformatics format
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open access Open Access ISSN: 15748936

Current Bioinformatics — Template for authors

Publisher: Bentham Science
Categories Rank Trend in last 3 yrs
Computational Mathematics #67 of 152 up up by 28 ranks
Genetics #221 of 325 up up by 52 ranks
Biochemistry #292 of 415 up up by 55 ranks
Molecular Biology #302 of 382 up up by 41 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 300 Published Papers | 808 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 08/06/2020
Insights & related journals
General info
Top papers
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

2.068

74% from 2018

Impact factor for Current Bioinformatics from 2016 - 2019
Year Value
2019 2.068
2018 1.189
2017 0.54
2016 0.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 74% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

2.7

29% from 2019

CiteRatio for Current Bioinformatics from 2016 - 2020
Year Value
2020 2.7
2019 2.1
2018 1.2
2017 1.2
2016 1.3
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 29% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.306

8% from 2019

SJR for Current Bioinformatics from 2016 - 2020
Year Value
2020 0.306
2019 0.333
2018 0.234
2017 0.251
2016 0.236
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 8% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

0.383

32% from 2019

SNIP for Current Bioinformatics from 2016 - 2020
Year Value
2020 0.383
2019 0.29
2018 0.205
2017 0.314
2016 0.301
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 32% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

open access Open Access ISSN: 13674803 e-ISSN: 13674811
recommended Recommended

Oxford University Press

CiteRatio: 9.9 | SJR: 3.599 | SNIP: 2.056
open access Open Access ISSN: 1469221X e-ISSN: 14693178
recommended Recommended

EMBO Press

CiteRatio: 10.6 | SJR: 4.584 | SNIP: 1.577
open access Open Access ISSN: 10967192 e-ISSN: 10967206

Elsevier

CiteRatio: 7.0 | SJR: 1.329 | SNIP: 1.627
open access Open Access ISSN: 13989219 e-ISSN: 16000854

Wiley

CiteRatio: 8.5 | SJR: 2.677 | SNIP: 1.204

Current Bioinformatics

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Bentham Science

Current Bioinformatics

Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews, drug clinical trial studies and guest edited thematic issues written by leaders in the field, covering a wide range o...... Read More

Mathematics

i
Last updated on
07 Jun 2020
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ISSN
1574-8936
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Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
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Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
Vancouver
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Citation Type
Numbered
[25]
i
Bibliography Example
Blonder, G E, Tinkham, M, & Klapwijk, T M. Transition from metallic to tunnel- ing regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B. 2013;87(10):100510.

Top papers written in this journal

Journal Article DOI: 10.2174/157489310794072508
A Review of Ensemble Methods in Bioinformatics
Pengyi Yang1, Yee Hwa Yang, Bing Bing Zhou, Albert Y. Zomaya
30 Nov 2010 - Current Bioinformatics

Abstract:

Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this ... Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed. read more read less

Topics:

Ensemble learning (73%)73% related to the paper, Feature selection (52%)52% related to the paper
364 Citations
open accessOpen access Journal Article DOI: 10.2174/157489312799304431
Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis.
Masahiro Sugimoto1, Masato Kawakami1, Martin Robert1, Tomoyoshi Soga, Masaru Tomita
29 Feb 2012 - Current Bioinformatics

Abstract:

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, ... Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader. read more read less
256 Citations
Journal Article DOI: 10.2174/157489307779314348
Hidden Markov Models in Bioinformatics
01 Jan 2007 - Current Bioinformatics

Abstract:

Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantage... Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics applications, such as alignment, labeling, and profiling of sequences, protein structure prediction, and pattern recognition. We finally provide a critical appraisal of the use and perspectives of HMMs in bioinformatics. read more read less

Topics:

Hidden Markov model (58%)58% related to the paper, Pattern recognition (psychology) (53%)53% related to the paper, Protein structure prediction (51%)51% related to the paper
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141 Citations
Journal Article DOI: 10.2174/157489306775330615
Gene Expression Profile Classification: A Review
01 Jan 2006 - Current Bioinformatics

Abstract:

In this review, we have discussed the class-prediction and discovery methods that are applied to gene expression data, along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of... In this review, we have discussed the class-prediction and discovery methods that are applied to gene expression data, along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important issues such as preprocessing of gene expression data, curse of dimensionality, feature extraction/selection, and measuring or estimating classifier performance. We discussed and summarized important properties such as generalizability (sensitivity to overtraining), built-in feature selection, ability to report prediction strength, and transparency (ease of understanding of the operation) of different class-predictor design approaches to provide a quick and concise reference. We have also covered the topic of biclustering, which is an emerging clustering method that processes the entries of the gene expression data matrix in both gene and sample directions simultaneously, in detail. read more read less

Topics:

Biclustering (56%)56% related to the paper, Feature selection (52%)52% related to the paper, Feature extraction (52%)52% related to the paper, Cluster analysis (51%)51% related to the paper
137 Citations
Journal Article DOI: 10.2174/157489309787158198
Molecular Genetic Markers: Discovery, Applications, Data Storage and Visualisation
Chris Duran, Nikki Appleby, David Edwards1, Jacqueline Batley
01 Jan 2009 - Current Bioinformatics

Abstract:

Molecular genetic markers represent one of the most powerful tools for the analysis of genomes and enable the association of heritable traits with underlying genomic variation. Molecular marker technology has developed rapidly over the last decade and two forms of sequence based marker, Simple Sequence Repeats (SSRs), also kn... Molecular genetic markers represent one of the most powerful tools for the analysis of genomes and enable the association of heritable traits with underlying genomic variation. Molecular marker technology has developed rapidly over the last decade and two forms of sequence based marker, Simple Sequence Repeats (SSRs), also known as microsatellites, and Single Nucleotide Polymorphisms (SNPs) now predominate applications in modern genetic analysis. The reducing cost of DNA sequencing has led to the availability of large sequence data sets derived from whole genome sequencing and large scale Expressed Sequence Tag (EST) discovery that enable the mining of SSRs and SNPs, which may then be applied to diversity analysis, genetic trait mapping, association studies, and marker assisted selection. These markers are inexpensive, require minimal labour to produce and can frequently be associated with annotated genes. Here we review automated methods for the discovery of SSRs and SNPs and provide an overview of the diverse applications of these markers. read more read less

Topics:

Molecular marker (56%)56% related to the paper, Genetic marker (55%)55% related to the paper, Whole genome sequencing (53%)53% related to the paper, Expressed sequence tag (53%)53% related to the paper, DNA sequencing (52%)52% related to the paper
136 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

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With SciSpace, you do not need a word template for Current Bioinformatics.

It automatically formats your research paper to Bentham Science formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

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Easy support from all your favorite tools

Current Bioinformatics format uses Vancouver citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Current Bioinformatics guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Current Bioinformatics citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Current Bioinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

The 5 most common citation types in order of usage are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Current Bioinformatics.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Current Bioinformatics Endnote style, according to bentham-science guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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