Example of Computational and Structural Biotechnology Journal format
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Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format
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Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format Example of Computational and Structural Biotechnology Journal format
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open access Open Access

Computational and Structural Biotechnology Journal — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Biophysics #31 of 131 down down by 14 ranks
Computer Science Applications #171 of 693 down down by 122 ranks
Biotechnology #90 of 282 down down by 52 ranks
Genetics #128 of 325 down down by 56 ranks
Biochemistry #173 of 415 down down by 91 ranks
Structural Biology #25 of 48 down down by 13 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 581 Published Papers | 2971 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 14/07/2020
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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.

5.1

4% from 2019

CiteRatio for Computational and Structural Biotechnology Journal from 2016 - 2020
Year Value
2020 5.1
2019 5.3
2018 8.7
2017 6.9
2016 4.2
graph view Graph view
table view Table view

1.908

7% from 2019

SJR for Computational and Structural Biotechnology Journal from 2016 - 2020
Year Value
2020 1.908
2019 1.782
2018 1.726
2017 1.517
2016 1.334
graph view Graph view
table view Table view

1.897

6% from 2019

SNIP for Computational and Structural Biotechnology Journal from 2016 - 2020
Year Value
2020 1.897
2019 1.785
2018 1.925
2017 1.087
2016 1.032
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 4% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 7% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 6% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Computational and Structural Biotechnology Journal

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Elsevier

Computational and Structural Biotechnology Journal

Computational and Structural Biotechnology Journal (CSBJ) is an online journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on function...... Read More

Proteomics

i
Last updated on
14 Jul 2020
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ISSN
2001-0370
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Impact Factor
High - 1.043
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Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. (1982). Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B. 25(7):4515–4532. Available from: 10.1103/PhysRevB.25.4515.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1016/J.CSBJ.2014.11.005
Machine learning applications in cancer prognosis and prediction.

Abstract:

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk... Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes. read more read less

Topics:

Decision tree (50%)50% related to the paper
View PDF
1,991 Citations
open accessOpen access Journal Article DOI: 10.1016/J.CSBJ.2016.12.005
Machine Learning and Data Mining Methods in Diabetes Research.

Abstract:

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more... The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM. read more read less

Topics:

Supervised learning (52%)52% related to the paper, Association rule learning (51%)51% related to the paper
811 Citations
open accessOpen access Journal Article DOI: 10.1016/J.CSBJ.2020.03.025
Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model
Bo Ram Beck, Bonggun Shin1, Yoon Jung Choi, Sung-Soo Park, Keunsoo Kang2

Abstract:

The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep le... The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd read more read less

Topics:

Atazanavir (57%)57% related to the paper, Darunavir (56%)56% related to the paper, Ritonavir (54%)54% related to the paper, Dolutegravir (54%)54% related to the paper, Lopinavir (54%)54% related to the paper
573 Citations
open accessOpen access Journal Article DOI: 10.1016/J.CSBJ.2018.01.001
Machine Learning Methods for Histopathological Image Analysis.
Daisuke Komura1, Shumpei Ishikawa1

Abstract:

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digita... Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions. read more read less

Topics:

Deep learning (52%)52% related to the paper
545 Citations
open accessOpen access Journal Article DOI: 10.1016/J.CSBJ.2018.07.004
FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data.
Peng Zhang1, Jules White1, Douglas C. Schmidt1, Gunther Lenz2, S. Trent Rosenbloom3

Abstract:

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of app... Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the "Shared Nationwide Interoperability Roadmap" from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study. read more read less

Topics:

Data sharing (55%)55% related to the paper, Interoperability (54%)54% related to the paper, Information exchange (53%)53% related to the paper, Health information technology (52%)52% related to the paper
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455 Citations
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Frequently asked questions

1. Can I write Computational and Structural Biotechnology Journal in LaTeX?

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

2. Do you follow the Computational and Structural Biotechnology Journal guidelines?

Yes, the template is compliant with the Computational and Structural Biotechnology Journal 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 Computational and Structural Biotechnology Journal?

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 Computational and Structural Biotechnology Journal citation style.

4. Can I use the Computational and Structural Biotechnology Journal 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 Computational and Structural Biotechnology Journal.

5. Can I use a manuscript in Computational and Structural Biotechnology Journal 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 Computational and Structural Biotechnology Journal that you can download at the end.

6. How long does it usually take you to format my papers in Computational and Structural Biotechnology Journal?

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

7. Where can I find the template for the Computational and Structural Biotechnology Journal?

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 Computational and Structural Biotechnology Journal'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 Computational and Structural Biotechnology Journal'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. Computational and Structural Biotechnology Journal an online tool or is there a desktop version?

SciSpace's Computational and Structural Biotechnology Journal 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 Computational and Structural Biotechnology Journal?

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 Computational and Structural Biotechnology Journal?”

11. What is the output that I would get after using Computational and Structural Biotechnology Journal?

After writing your paper autoformatting in Computational and Structural Biotechnology Journal, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computational and Structural Biotechnology Journal'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 Computational and Structural Biotechnology Journal?

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 Computational and Structural Biotechnology Journal. 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 Computational and Structural Biotechnology Journal?

The 5 most common citation types in order of usage for Computational and Structural Biotechnology Journal 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 Computational and Structural Biotechnology Journal?

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 Computational and Structural Biotechnology Journal's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Computational and Structural Biotechnology Journal 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 Computational and Structural Biotechnology Journal Endnote style according to Elsevier guidelines.

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