Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format
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Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format
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Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format Example of IEEE/ACM Transactions on Computational Biology and Bioinformatics format
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IEEE/ACM Transactions on Computational Biology and Bioinformatics — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Applied Mathematics #29 of 548 up up by 28 ranks
Biotechnology #67 of 282 up up by 20 ranks
Genetics #80 of 325 up up by 83 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 724 Published Papers | 4618 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/06/2020
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Related Journals

open access Open Access

Springer

Quality:  
High
CiteRatio: 6.4
SJR: 1.547
SNIP: 1.162
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Quality:  
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CiteRatio: 4.1
SJR: 0.833
SNIP: 0.855
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Springer

Quality:  
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CiteRatio: 6.8
SJR: 1.35
SNIP: 1.956
open access Open Access

Springer

Quality:  
High
CiteRatio: 4.0
SJR: 0.724
SNIP: 0.788

Journal Performance & Insights

Impact Factor

CiteRatio

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.

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

3.015

4% from 2018

Impact factor for IEEE/ACM Transactions on Computational Biology and Bioinformatics from 2016 - 2019
Year Value
2019 3.015
2018 2.896
2017 2.428
2016 1.955
graph view Graph view
table view Table view

6.4

21% from 2019

CiteRatio for IEEE/ACM Transactions on Computational Biology and Bioinformatics from 2016 - 2020
Year Value
2020 6.4
2019 5.3
2018 3.9
2017 3.8
2016 3.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 4% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 21% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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.

0.745

0% from 2019

SJR for IEEE/ACM Transactions on Computational Biology and Bioinformatics from 2016 - 2020
Year Value
2020 0.745
2019 0.748
2018 0.651
2017 0.649
2016 0.735
graph view Graph view
table view Table view

1.278

7% from 2019

SNIP for IEEE/ACM Transactions on Computational Biology and Bioinformatics from 2016 - 2020
Year Value
2020 1.278
2019 1.193
2018 1.09
2017 1.007
2016 0.921
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 0% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 7% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
IEEE/ACM Transactions on Computational Biology and Bioinformatics

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IEEE

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Approved by publishing and review experts on SciSpace, this template is built as per for IEEE/ACM Transactions on Computational Biology and Bioinformatics formatting guidelines as mentioned in IEEE author instructions. The current version was created on 09 Jun 2020 and has been used by 755 authors to write and format their manuscripts to this journal.

Mathematics

i
Last updated on
09 Jun 2020
i
ISSN
1545-5963
i
Impact Factor
High - 1.152
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/TCBB.2004.2
Biclustering Algorithms for Biological Data Analysis: A Survey
Sara C. Madeira, Arlindo L. Oliveira1

Abstract:

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the a... A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications. read more read less

Topics:

Biclustering (73%)73% related to the paper, Correlation clustering (66%)66% related to the paper, Clustering high-dimensional data (65%)65% related to the paper, Cluster analysis (62%)62% related to the paper, Fuzzy clustering (58%)58% related to the paper
View PDF
2,123 Citations
Journal Article DOI: 10.1109/TCBB.2012.33
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

Abstract:

A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioin... A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them in a unified framework, using standardized notations in order to reveal their technical details and to highlight their common characteristics as well as their particularities. read more read less

Topics:

Feature selection (56%)56% related to the paper
500 Citations
open accessOpen access Journal Article DOI: 10.1109/TCBB.2021.3065361
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.

Abstract:

A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. ... A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. A deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model can accurately identify the COVID-19 patients from the healthy with an AUC of 0.99, recall (sensitivity) of 0.93, and precision of 0.96. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO) that is visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/model.php. read more read less
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477 Citations
Journal Article DOI: 10.1109/TCBB.2015.2478454
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
Jun Chin Ang1, Andri Mirzal2, Habibollah Haron1, Haza Nuzly Abdull Hamed1

Abstract:

Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Becaus... Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection. read more read less

Topics:

Feature selection (63%)63% related to the paper, Dimensionality reduction (60%)60% related to the paper, Feature extraction (56%)56% related to the paper, Statistical classification (56%)56% related to the paper
402 Citations
Journal Article DOI: 10.1109/TCBB.2006.51
Efficient Detection of Network Motifs
Sebastian Wernicke1

Abstract:

Motifs in a given network are small connected subnetworks that occur in significantly higher frequencies than would be expected in random networks. They have recently gathered much attention as a concept to uncover structural design principles of complex networks. Kashtan et al. [Bioinformatics, 2004] proposed a sampling algo... Motifs in a given network are small connected subnetworks that occur in significantly higher frequencies than would be expected in random networks. They have recently gathered much attention as a concept to uncover structural design principles of complex networks. Kashtan et al. [Bioinformatics, 2004] proposed a sampling algorithm for performing the computationally challenging task of detecting network motifs. However, among other drawbacks, this algorithm suffers from a sampling bias and scales poorly with increasing subgraph size. Based on a detailed analysis of the previous algorithm, we present a new algorithm for network motif detection which overcomes these drawbacks. Furthermore, we present an efficient new approach for estimating the frequency of subgraphs in random networks that, in contrast to previous approaches, does not require the explicit generation of random networks. Experiments on a testbed of biological networks show our new algorithms to be orders of magnitude faster than previous approaches, allowing for the detection of larger motifs in bigger networks than previously possible and thus facilitating deeper insight into the field. read more read less

Topics:

Network motif (64%)64% related to the paper, Complex network (60%)60% related to the paper, Biological network (59%)59% related to the paper
378 Citations
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Frequently asked questions

1. Can I write IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics guidelines and auto format it.

2. Do you follow the IEEE/ACM Transactions on Computational Biology and Bioinformatics guidelines?

Yes, the template is compliant with the IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics citation style.

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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 IEEE/ACM Transactions on Computational Biology and Bioinformatics.

5. Can I use a manuscript in IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics that you can download at the end.

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It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE/ACM Transactions on Computational Biology and Bioinformatics.

7. Where can I find the template for the IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

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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. IEEE/ACM Transactions on Computational Biology and Bioinformatics an online tool or is there a desktop version?

SciSpace's IEEE/ACM Transactions on Computational Biology and 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.

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After writing your paper autoformatting in IEEE/ACM Transactions on Computational Biology and Bioinformatics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics?

The 5 most common citation types in order of usage for IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics?

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16. Can I download IEEE/ACM Transactions on Computational Biology and 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 IEEE/ACM Transactions on Computational Biology and Bioinformatics Endnote style according to Elsevier guidelines.

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