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Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format Example of BMC Proceedings format
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open access Open Access

BMC Proceedings — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Biochemistry, Genetics and Molecular Biology (all) #91 of 204 up up by 8 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 112 Published Papers | 343 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 23/06/2020
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Related Journals

open access Open Access

SAGE

Quality:  
High
CiteRatio: 5.7
SJR: 1.012
SNIP: 0.851
open access Open Access
recommended Recommended

PLOS

Quality:  
High
CiteRatio: 11.0
SJR: 4.127
SNIP: 2.005
open access Open Access
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The National Academies of Sciences, Engineering, and Medicine

Quality:  
High
CiteRatio: 6.0
SJR: 1.129
SNIP: 1.292
open access Open Access
recommended Recommended

Nature

Quality:  
High
CiteRatio: 20.0
SJR: 5.559
SNIP: 3.055

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.

3.1

72% from 2019

CiteRatio for BMC Proceedings from 2016 - 2020
Year Value
2020 3.1
2019 1.8
2018 1.6
2017 2.2
2016 0.2
graph view Graph view
table view Table view

0.79

128% from 2019

SJR for BMC Proceedings from 2016 - 2020
Year Value
2020 0.79
2019 0.347
2018 0.35
2017 0.302
2016 0.516
graph view Graph view
table view Table view

0.693

128% from 2019

SNIP for BMC Proceedings from 2016 - 2020
Year Value
2020 0.693
2019 0.304
2018 0.335
2017 0.23
2016 0.477
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 72% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 128% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 128% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

BMC Proceedings

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Springer

BMC Proceedings

Approved by publishing and review experts on SciSpace, this template is built as per for BMC Proceedings formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 241 authors to write and format their manuscripts to this journal.

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Last updated on
22 Jun 2020
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ISSN
1606-8610
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/1753-6561-5-S7-P54
Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus
13 Sep 2011 - BMC Proceedings

Abstract:

Background Wider genome coverage and higher throughput genotyping methods have become increasingly important to meet the resolution and speed necessary for a variety of applications in genomics and molecular breeding of forest trees. Developed more than 10 years ago [1], the Diversity Arrays Technology (DArT) has experienced ... Background Wider genome coverage and higher throughput genotyping methods have become increasingly important to meet the resolution and speed necessary for a variety of applications in genomics and molecular breeding of forest trees. Developed more than 10 years ago [1], the Diversity Arrays Technology (DArT) has experienced an increasing interest worldwide for it has efficiently satisfied the requirements of throughput, genome coverage and inter-specific transferability for over 40 different plant species to date, including Eucalyptus[2] and recently Pinus (Dione Alves-Freitas, this meeting). DArT is based on genome complexity reduction using restriction enzymes, followed by hybridization to microarrays to simultaneously assay hundreds to thousands of markers across a genome. Genome complexity reduction for genotyping has now been taken to another level when combined to next generation sequencing (NGS) technologies. Such a strategy has been used for rapid SNP discovery in different organisms [3], and proposed as a way to genotype with RAD (Restriction-associated DNA) sequencing [4]and recently by a similar method generally termed GbS (Genotyping-by-Sequencing)[5]. In this work we assessed the power of the now well established DArT marker platform in combination with Illumina short read sequencing to generate a linkage map for a segregating outcrossed F1 population derived from E. grandis BRASUZ1, the donor of the Eucalyptus reference genome. read more read less

Topics:

Reference genome (62%)62% related to the paper, Genomics (57%)57% related to the paper, Diversity Arrays Technology (55%)55% related to the paper, Genome (54%)54% related to the paper, Molecular breeding (53%)53% related to the paper
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298 Citations
open accessOpen access Journal Article DOI: 10.1186/1753-6561-6-S2-S10
Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions
Joseph O. Ogutu1, Torben Schulz-Streeck1, Hans-Peter Piepho1
21 May 2012 - BMC Proceedings

Abstract:

Background Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total g... Background Genomic selection (GS) is emerging as an efficient and cost-effective method for estimating breeding values using molecular markers distributed over the entire genome. In essence, it involves estimating the simultaneous effects of all genes or chromosomal segments and combining the estimates to predict the total genomic breeding value (GEBV). Accurate prediction of GEBVs is a central and recurring challenge in plant and animal breeding. The existence of a bewildering array of approaches for predicting breeding values using markers underscores the importance of identifying approaches able to efficiently and accurately predict breeding values. Here, we comparatively evaluate the predictive performance of six regularized linear regression methods-- ridge regression, ridge regression BLUP, lasso, adaptive lasso, elastic net and adaptive elastic net-- for predicting GEBV using dense SNP markers. read more read less

Topics:

Elastic net regularization (70%)70% related to the paper, Lasso (statistics) (63%)63% related to the paper, Linear regression (53%)53% related to the paper, Best linear unbiased prediction (51%)51% related to the paper
View PDF
274 Citations
open accessOpen access Journal Article DOI: 10.1186/1753-6561-5-S3-S11
A comparison of random forests, boosting and support vector machines for genomic selection
Joseph O. Ogutu1, Hans-Peter Piepho1, Torben Schulz-Streeck1
27 May 2011 - BMC Proceedings

Abstract:

Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values m... Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative predictive performances to identify approaches able to accurately predict breeding values. We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs. We predicted GEBVs for one quantitative trait in a dataset simulated for the QTLMAS 2010 workshop. Predictive accuracy was measured as the Pearson correlation between GEBVs and observed values using 5-fold cross-validation and between predicted and true breeding values. The importance of each marker was ranked using RF and plotted against the position of the marker and associated QTLs on one of five simulated chromosomes. The correlations between the predicted and true breeding values were 0.547 for boosting, 0.497 for SVMs, and 0.483 for RF, indicating better performance for boosting than for SVMs and RF. Accuracy was highest for boosting, intermediate for SVMs and lowest for RF but differed little among the three methods and relative to ridge regression BLUP (RR-BLUP). read more read less

Topics:

True breeding organism (51%)51% related to the paper
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210 Citations
open accessOpen access Journal Article DOI: 10.1186/1753-6561-5-S9-S2
Genetic Analysis Workshop 17 mini-exome simulation.
29 Nov 2011 - BMC Proceedings

Abstract:

The data set simulated for Genetic Analysis Workshop 17 was designed to mimic a subset of data that might be produced in a full exome screen for a complex disorder and related risk factors in order to permit workshop participants to investigate issues of study design and statistical genetic analysis. Real sequence data from t... The data set simulated for Genetic Analysis Workshop 17 was designed to mimic a subset of data that might be produced in a full exome screen for a complex disorder and related risk factors in order to permit workshop participants to investigate issues of study design and statistical genetic analysis. Real sequence data from the 1000 Genomes Project formed the basis for simulating a common disease trait with a prevalence of 30% and three related quantitative risk factors in a sample of 697 unrelated individuals and a second sample of 697 individuals in large, extended pedigrees. Called genotypes for 24,487 autosomal markers assigned to 3,205 genes and simulated affection status, quantitative traits, age, sex, pedigree relationships, and cigarette smoking were provided to workshop participants. The simulating model included both common and rare variants with minor allele frequencies ranging from 0.07% to 25.8% and a wide range of effect sizes for these variants. Genotype-smoking interaction effects were included for variants in one gene. Functional variants were concentrated in genes selected from specific biological pathways and were selected on the basis of the predicted deleteriousness of the coding change. For each sample, unrelated individuals and family, 200 replicates of the phenotypes were simulated. read more read less

Topics:

1000 Genomes Project (55%)55% related to the paper, Exome (53%)53% related to the paper, Minor allele frequency (52%)52% related to the paper, Quantitative trait locus (51%)51% related to the paper
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172 Citations
open accessOpen access Journal Article DOI: 10.1186/1753-6561-3-S4-S6
Biological pathway analysis by ArrayUnlock and Ingenuity Pathway Analysis
16 Jul 2009 - BMC Proceedings

Abstract:

Once a list of differentially expressed genes has been identified from a microarray experiment, a subsequent post-analysis task is required in order to find the main biological processes associated to the experimental system. This paper describes two pathways analysis tools, ArrayUnlock and Ingenuity Pathways Analysis (IPA) t... Once a list of differentially expressed genes has been identified from a microarray experiment, a subsequent post-analysis task is required in order to find the main biological processes associated to the experimental system. This paper describes two pathways analysis tools, ArrayUnlock and Ingenuity Pathways Analysis (IPA) to deal with the post-analyses of microarray data, in the context of the EADGENE and SABRE post-analysis workshop. Dataset employed in this study proceeded from an experimental chicken infection performed to study the host reactions after a homologous or heterologous secondary challenge with two species of Eimeria. Analysis of the same microarray data source employing both commercial pathway analysis tools in parallel let to identify several biological and/or molecular functions altered in the chicken Eimeria maxima infection model, including several immune system related pathways. Biological functions differentially altered in the homologous and heterologous second infection were identified. Similarly, the effect of the timing in a homologous second infection was characterized by several biological functions. Functional analysis with ArrayUnlock and IPA provided information related to functional differences with the three comparisons of the chicken infection leading to similar conclusions. ArrayUnlock let an improvement of the annotations of the chicken genome adding InterPro annotations to the data set file. IPA provides two powerful tools to understand the pathway analysis results: the networks and canonical pathways that showed several pathways related to an adaptative immune response. read more read less

Topics:

InterPro (50%)50% related to the paper, Microarray analysis techniques (50%)50% related to the paper
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151 Citations
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Frequently asked questions

1. Can I write BMC Proceedings in LaTeX?

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

2. Do you follow the BMC Proceedings guidelines?

Yes, the template is compliant with the BMC Proceedings 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 BMC Proceedings?

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 BMC Proceedings citation style.

4. Can I use the BMC Proceedings 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 BMC Proceedings.

5. Can I use a manuscript in BMC Proceedings 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 BMC Proceedings that you can download at the end.

6. How long does it usually take you to format my papers in BMC Proceedings?

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

7. Where can I find the template for the BMC Proceedings?

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 BMC Proceedings'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 BMC Proceedings'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. BMC Proceedings an online tool or is there a desktop version?

SciSpace's BMC Proceedings 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 BMC Proceedings?

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 BMC Proceedings?”

11. What is the output that I would get after using BMC Proceedings?

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

12. Is BMC Proceedings'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 BMC Proceedings?

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 BMC Proceedings. 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 BMC Proceedings?

The 5 most common citation types in order of usage for BMC Proceedings 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 BMC Proceedings?

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 BMC Proceedings's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download BMC Proceedings 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 BMC Proceedings 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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