Example of Advances in Bioinformatics format
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Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format
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Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format Example of Advances in Bioinformatics format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Advances in Bioinformatics — Template for authors

Publisher: Hindawi
Categories Rank Trend in last 3 yrs
Biochemistry, Genetics and Molecular Biology (miscellaneous) #8 of 46 -
Computer Science Applications #157 of 693 up up by 40 ranks
Biomedical Engineering #70 of 229 up up by 11 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 18 Published Papers | 97 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 27/06/2020
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Related Journals

open access Open Access
recommended Recommended

American Chemical Society

Quality:  
High
CiteRatio: 7.9
SJR: 2.156
SNIP: 1.033
open access Open Access
recommended Recommended

Nature

Quality:  
High
CiteRatio: 28.0
SJR: 5.961
SNIP: 3.528
open access Open Access

Frontiers Media

Quality:  
High
CiteRatio: 6.2
SJR: 1.144
SNIP: 1.364

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

5.4

50% from 2019

CiteRatio for Advances in Bioinformatics from 2016 - 2020
Year Value
2020 5.4
2019 3.6
2018 4.2
2017 3.1
2016 1.8
graph view Graph view
table view Table view

0.33

15% from 2019

SJR for Advances in Bioinformatics from 2016 - 2020
Year Value
2020 0.33
2019 0.39
2018 0.522
2017 0.565
2016 0.49
graph view Graph view
table view Table view

1.281

56% from 2019

SNIP for Advances in Bioinformatics from 2016 - 2020
Year Value
2020 1.281
2019 0.821
2018 1.295
2017 0.869
2016 0.562
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 50% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 15% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 56% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Advances in Bioinformatics

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Hindawi

Advances in Bioinformatics

Advances in Bioinformatics is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of bioinformatics.... Read More

Computer Science

i
Last updated on
27 Jun 2020
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ISSN
1687-8027
i
Impact Factor
Low - 0.442
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Acceptance Rate
22%
i
Frequency
Not provided
i
Open Access
Yes
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
unsrt
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Citation Type
Numbered
[25]
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Bibliography Example
C. W. J. Beenakker. “Specular andreev reflection in graphene”. Phys. Rev. Lett., vol. 97, no. 6, 067007, 2006.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1155/2008/420747
Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes.

Abstract:

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computa... The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into expression modules, and to model expression responses in the context of metabolic and regulatory networks. Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery research and hypothesis validation. read more read less

Topics:

Microarray analysis techniques (51%)51% related to the paper
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1,859 Citations
open accessOpen access Journal Article DOI: 10.1155/2015/198363
A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data.
Zena M. Hira1, Duncan Fyfe Gillies1

Abstract:

We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accu... We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources. read more read less

Topics:

Feature extraction (57%)57% related to the paper, Dimensionality reduction (57%)57% related to the paper, Feature selection (56%)56% related to the paper
View PDF
749 Citations
open accessOpen access Journal Article DOI: 10.1155/2009/247646
Merging Mixture Components for Cell Population Identification in Flow Cytometry
Greg Finak, Ali Bashashati, Ryan R. Brinkman, Raphael Gottardo1

Abstract:

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than ei... We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project. read more read less

Topics:

Mixture model (55%)55% related to the paper, Population (51%)51% related to the paper
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123 Citations
open accessOpen access Journal Article DOI: 10.1155/2009/584603
A Survey of Flow Cytometry Data Analysis Methods
Ali Bashashati1, Ryan R. Brinkman1

Abstract:

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic s... Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined. read more read less
View PDF
120 Citations
open accessOpen access Journal Article DOI: 10.1155/2008/205969
Metagenome Fragment Classification Using N-Mer Frequency Profiles
Gail L. Rosen1, Elaine Garbarine1, Diamantino Caseiro2, Robi Polikar3, Bahrad A. Sokhansanj1

Abstract:

A vast amount of microbial sequencing data is being generated through large-scale projects in ecology, agriculture, and human health. Efficient high-throughput methods are needed to analyze the mass amounts of metagenomic data, all DNA present in an environmental sample. A major obstacle in metagenomics is the inability to ob... A vast amount of microbial sequencing data is being generated through large-scale projects in ecology, agriculture, and human health. Efficient high-throughput methods are needed to analyze the mass amounts of metagenomic data, all DNA present in an environmental sample. A major obstacle in metagenomics is the inability to obtain accuracy using technology that yields short reads. We construct the unique N-mer frequency profiles of 635 microbial genomes publicly available as of February 2008. These profiles are used to train a naive Bayes classifier (NBC) that can be used to identify the genome of any fragment. We show that our method is comparable to BLAST for small 25 bp fragments but does not have the ambiguity of BLAST's tied top scores. We demonstrate that this approach is scalable to identify any fragment from hundreds of genomes. It also performs quite well at the strain, species, and genera levels and achieves strain resolution despite classifying ubiquitous genomic fragments (gene and nongene regions). Cross-validation analysis demonstrates that species-accuracy achieves 90% for highly-represented species containing an average of 8 strains. We demonstrate that such a tool can be used on the Sargasso Sea dataset, and our analysis shows that NBC can be further enhanced. read more read less

Topics:

Metagenomics (56%)56% related to the paper
View PDF
114 Citations
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Advances in Bioinformatics format uses unsrt citation style.

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

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Frequently asked questions

1. Can I write Advances in Bioinformatics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Advances in Bioinformatics guidelines and auto format it.

2. Do you follow the Advances in Bioinformatics guidelines?

Yes, the template is compliant with the Advances in Bioinformatics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Advances in Bioinformatics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Advances in Bioinformatics citation style.

4. Can I use the Advances in Bioinformatics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Advances in Bioinformatics.

5. Can I use a manuscript in Advances in Bioinformatics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Advances in Bioinformatics that you can download at the end.

6. How long does it usually take you to format my papers in Advances in Bioinformatics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Advances in Bioinformatics.

7. Where can I find the template for the Advances in Bioinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Advances in Bioinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Advances in Bioinformatics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Advances in Bioinformatics an online tool or is there a desktop version?

SciSpace's Advances in Bioinformatics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Advances in Bioinformatics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Advances in Bioinformatics?”

11. What is the output that I would get after using Advances in Bioinformatics?

After writing your paper autoformatting in Advances in Bioinformatics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Advances in Bioinformatics's impact factor high enough that I should try publishing my article there?

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 these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Advances in Bioinformatics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Advances in Bioinformatics. The table below indicates the level of access a journal has as per Sherpa Romeo's 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.

14. What are the most common citation types In Advances in Bioinformatics?

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

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

15. How do I submit my article to the Advances in Bioinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Advances in Bioinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Advances in Bioinformatics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Advances in Bioinformatics Endnote style according to Elsevier 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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