Example of Cancer Research format
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Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format
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Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format Example of Cancer Research format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access
recommended Recommended

Cancer Research — Template for authors

Categories Rank Trend in last 3 yrs
Oncology #21 of 340 down down by 2 ranks
Cancer Research #15 of 207 down down by 1 rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 2233 Published Papers | 35343 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 01/07/2020
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Related Journals

open access Open Access
recommended Recommended

Nature

Quality:  
High
CiteRatio: 16.0
SJR: 4.539
SNIP: 2.28
open access Open Access
recommended Recommended

American Association for Cancer Research

Quality:  
High
CiteRatio: 18.2
SJR: 5.427
SNIP: 2.243
open access Open Access

American Association for Cancer Research

Quality:  
High
CiteRatio: 8.7
SJR: 2.273
SNIP: 1.157
open access Open Access

American Association for Cancer Research

Quality:  
High
CiteRatio: 10.3
SJR: 2.717
SNIP: 1.313

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.

9.727

16% from 2018

Impact factor for Cancer Research from 2016 - 2019
Year Value
2019 9.727
2018 8.378
2017 9.13
2016 9.122
graph view Graph view
table view Table view

15.8

17% from 2019

CiteRatio for Cancer Research from 2016 - 2020
Year Value
2020 15.8
2019 13.5
2018 12.9
2017 13.8
2016 15.5
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 16% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 17% 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.

4.103

1% from 2019

SJR for Cancer Research from 2016 - 2020
Year Value
2020 4.103
2019 4.051
2018 4.047
2017 4.26
2016 4.908
graph view Graph view
table view Table view

1.983

9% from 2019

SNIP for Cancer Research from 2016 - 2020
Year Value
2020 1.983
2019 1.811
2018 1.637
2017 1.714
2016 1.999
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 1% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Cancer Research

Guideline source: View

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American Association for Cancer Research

Cancer Research

Cancer Research is the most frequently cited cancer journal in the world. Cancer Research seeks manuscripts that offer pathobiological and translational impact to inform the personal, clinical, and societal problems posed by cancer. The main scope of the Journal is captured in...... Read More

Oncology

Cancer Research

Medicine

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Last updated on
01 Jul 2020
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ISSN
0008-5472
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Impact Factor
High - 2.07
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Acceptance Rate
22%
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Open Access
No
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Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
Vancouver
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent con-version. Phys Rev B. 1982;25(7):4515–4532. Available from: 10.1103/PhysRevB.25.4515.

Top papers written in this journal

open accessOpen access Journal Article
The Detection of Disease Clustering and a Generalized Regression Approach
01 Feb 1967 - Cancer Research

Abstract:

The problem of identifying subtle time-space clustering of disease, as may be occurring in leukemia, is described and reviewed. Published approaches, generally associated with studies of leukemia, not dependent on knowledge of the underlying population for their validity, are directed towards identifying clustering by establi... The problem of identifying subtle time-space clustering of disease, as may be occurring in leukemia, is described and reviewed. Published approaches, generally associated with studies of leukemia, not dependent on knowledge of the underlying population for their validity, are directed towards identifying clustering by establishing a relationship between the temporal and the spatial separations for the n ( n - 1)/2 possible pairs which can be formed from the n observed cases of disease. Here it is proposed that statistical power can be improved by applying a reciprocal transform to these separations. While a permutational approach can give valid probability levels for any observed association, for reasons of practicability, it is suggested that the observed association be tested relative to its permutational variance. Formulas and computational procedures for doing so are given. While the distance measures between points represent symmetric relationships subject to mathematical and geometric regularities, the variance formula developed is appropriate for arbitrary relationships. Simplified procedures are given for the case of symmetric and skew-symmetric relationships. The general procedure is indicated as being potentially useful in other situations as, for example, the study of interpersonal relationships. Viewing the procedure as a regression approach, the possibility for extending it to nonlinear and multivariate situations is suggested. Other aspects of the problem and of the procedure developed are discussed. Similarly, pure temporal clustering can be identified by a study of incidence rates in periods of widespread epidemics. In point of fact, many epidemics of communicable diseases are somewhat local in nature and so these do actually constitute temporal-spatial clusters. For leukemia and similar diseases in which cases seem to arise substantially at random rather than as clear-cut epidemics, it is necessary to devise sensitive and efficient procedures for detecting any nonrandom component of disease occurrence. Various ingenious procedures which statisticians have developed for the detection of disease clustering are reviewed here. These procedures can be generalized so as to increase their statistical validity and efficiency. The technic to be given below for imparting statistical validity to the procedures already in vogue can be viewed as a generalized form of regression with possible useful application to problems arising in quite different contexts. read more read less

Topics:

Space-Time Clustering (55%)55% related to the paper, Cluster analysis (53%)53% related to the paper, Population (51%)51% related to the paper
View PDF
11,408 Citations
open accessOpen access Journal Article
A New Concept for Macromolecular Therapeutics in Cancer Chemotherapy: Mechanism of Tumoritropic Accumulation of Proteins and the Antitumor Agent Smancs
Yasuhiro Matsumura, Hiroshi Maeda1
01 Dec 1986 - Cancer Research

Abstract:

We previously found that a polymer conjugated to the anticancer protein neocarzinostatin, named smancs, accumulated more in tumor tissues than did neocarzinostatin. To determine the general mechanism of this tumoritropic accumulation of smancs and other proteins, we used radioactive (51Cr-labeled) proteins of various molecula... We previously found that a polymer conjugated to the anticancer protein neocarzinostatin, named smancs, accumulated more in tumor tissues than did neocarzinostatin. To determine the general mechanism of this tumoritropic accumulation of smancs and other proteins, we used radioactive (51Cr-labeled) proteins of various molecular sizes (Mr 12,000 to 160,000) and other properties. In addition, we used dye-complexed serum albumin to visualize the accumulation in tumors of tumor-bearing mice. Many proteins progressively accumulated in the tumor tissues of these mice, and a ratio of the protein concentration in the tumor to that in the blood of 5 was obtained within 19 to 72 h. A large protein like immunoglobulin G required a longer time to reach this value of 5. The protein concentration ratio in the tumor to that in the blood of neither 1 nor 5 was achieved with neocarzinostatin, a representative of a small protein (Mr 12,000) in all time. We speculate that the tumoritropic accumulation of these proteins resulted because of the hypervasculature, an enhanced permeability to even macromolecules, and little recovery through either blood vessels or lymphatic vessels. This accumulation of macromolecules in the tumor was also found after i.v. injection of an albumin-dye complex (Mr 69,000), as well as after injection into normal and tumor tissues. The complex was retained only by tumor tissue for prolonged periods. There was little lymphatic recovery of macromolecules from tumor tissue. The present finding is of potential value in macromolecular tumor therapeutics and diagnosis. read more read less

Topics:

Enhanced permeability and retention effect (62%)62% related to the paper, Neocarzinostatin (53%)53% related to the paper
View PDF
6,483 Citations
Journal Article DOI: 10.1158/0008-5472.CAN-04-0496
Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets
Claus L. Andersen1, Jens Ledet Jensen1, Torben F. Ørntoft1
01 Aug 2004 - Cancer Research

Abstract:

Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear t... Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data. read more read less

Topics:

Normalization (statistics) (62%)62% related to the paper, Reference genes (53%)53% related to the paper
View PDF
6,007 Citations
open accessOpen access Journal Article
ras Oncogenes in Human Cancer: A Review
Joyce J. F. J. Bos1
01 Sep 1989 - Cancer Research

Abstract:

Mutations in codon 12, 13, or 61 of one of the three ras genes, H-ras, K-ras, and N-ras, convert these genes into active oncogenes. Rapid assays for the detection of these point mutations have been developed recently and used to investigate the role mutated ras genes play in the pathogenesis of human tumors. It appeared that ... Mutations in codon 12, 13, or 61 of one of the three ras genes, H-ras, K-ras, and N-ras, convert these genes into active oncogenes. Rapid assays for the detection of these point mutations have been developed recently and used to investigate the role mutated ras genes play in the pathogenesis of human tumors. It appeared that ras gene mutations can be found in a variety of tumor types, although the incidence varies greatly. The highest incidences are found in adenocarcinomas of the pancreas (90%), the colon (50%), and the lung (30%); in thyroid tumors (50%); and in myeloid leukemia (30%). For some tumor types a relationship may exist between the presence of a ras mutation and clinical or histopathological features of the tumor. There is some evidence that environmental agents may be involved in the induction of the mutations. read more read less

Topics:

Neuroblastoma RAS viral oncogene homolog (60%)60% related to the paper, Anti-apoptotic Ras signalling cascade (59%)59% related to the paper, P120 GTPase Activating Protein (54%)54% related to the paper, Gene mutation (53%)53% related to the paper, Point mutation (53%)53% related to the paper
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5,367 Citations
open accessOpen access Journal Article DOI: 10.1158/0008-5472.CAN-14-0155
Projecting Cancer Incidence and Deaths to 2030: The Unexpected Burden of Thyroid, Liver, and Pancreas Cancers in the United States
01 Jun 2014 - Cancer Research

Abstract:

Cancer incidence and deaths in the United States were projected for the most common cancer types for the years 2020 and 2030 based on changing demographics and the average annual percentage changes in incidence and death rates. Breast, prostate, and lung cancers will remain the top cancer diagnoses throughout this time, but t... Cancer incidence and deaths in the United States were projected for the most common cancer types for the years 2020 and 2030 based on changing demographics and the average annual percentage changes in incidence and death rates. Breast, prostate, and lung cancers will remain the top cancer diagnoses throughout this time, but thyroid cancer will replace colorectal cancer as the fourth leading cancer diagnosis by 2030, and melanoma and uterine cancer will become the fifth and sixth most common cancers, respectively. Lung cancer is projected to remain the top cancer killer throughout this time period. However, pancreas and liver cancers are projected to surpass breast, prostate, and colorectal cancers to become the second and third leading causes of cancer-related death by 2030, respectively. Advances in screening, prevention, and treatment can change cancer incidence and/or death rates, but it will require a concerted effort by the research and healthcare communities now to effect a substantial change for the future. read more read less

Topics:

Epidemiology of cancer (67%)67% related to the paper, Cancer (66%)66% related to the paper, Uterine cancer (63%)63% related to the paper, Thyroid cancer (56%)56% related to the paper, Lung cancer (53%)53% related to the paper
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4,973 Citations
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With SciSpace, you do not need a word template for Cancer Research.

It automatically formats your research paper to American Association for Cancer Research 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

Plagiarism Reports via Turnitin

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Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

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Cancer Research 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

1. Can I write Cancer Research in LaTeX?

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

2. Do you follow the Cancer Research guidelines?

Yes, the template is compliant with the Cancer Research 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 Cancer Research?

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 Cancer Research citation style.

4. Can I use the Cancer Research 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 Cancer Research.

5. Can I use a manuscript in Cancer Research 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 Cancer Research that you can download at the end.

6. How long does it usually take you to format my papers in Cancer Research?

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

7. Where can I find the template for the Cancer Research?

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 Cancer Research'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 Cancer Research'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. Cancer Research an online tool or is there a desktop version?

SciSpace's Cancer Research 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 Cancer Research?

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 Cancer Research?”

11. What is the output that I would get after using Cancer Research?

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

12. Is Cancer Research'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 Cancer Research?

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 Cancer Research. 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 Cancer Research?

The 5 most common citation types in order of usage for Cancer Research 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 Cancer Research?

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

16. Can I download Cancer Research 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 Cancer Research 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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