Example of Cancer Research format
Recent searches

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.
Look Inside
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 ISSN: 85472 e-ISSN: 15387445
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
Insights & related journals
General info
Top papers
Popular templates
Get started guide
Why choose from SciSpace
FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor 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.

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

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.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

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

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

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

insights Insights

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

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

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

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

Related Journals

open access Open Access ISSN: 8876924 e-ISSN: 14765551
recommended Recommended

Nature

CiteRatio: 16.0 | SJR: 4.539 | SNIP: 2.28
open access Open Access ISSN: 10780432 e-ISSN: 15573265
recommended Recommended

American Association for Cancer Research

CiteRatio: 18.2 | SJR: 5.427 | SNIP: 2.243
open access Open Access ISSN: 15417786 e-ISSN: 15573125

American Association for Cancer Research

CiteRatio: 8.7 | SJR: 2.273 | SNIP: 1.157
open access Open Access ISSN: 15357163 e-ISSN: 15388514

American Association for Cancer Research

CiteRatio: 10.3 | SJR: 2.717 | SNIP: 1.313

Cancer Research

Guideline source: View

All company, product and service names used in this website are for identification purposes only. All product names, trademarks and registered trademarks are property of their respective owners.

Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

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

i
Last updated on
01 Jul 2020
i
ISSN
0008-5472
i
Impact Factor
High - 2.07
i
Acceptance Rate
22%
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
Vancouver
i
Citation Type
Numbered
[25]
i
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
10,970 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
6,118 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
5,294 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
View PDF
5,285 Citations
open accessOpen access Journal Article
Identification of a Cancer Stem Cell in Human Brain Tumors
15 Sep 2003 - Cancer Research

Abstract:

Most current research on human brain tumors is focused on the molecular and cellular analysis of the bulk tumor mass. However, there is overwhelming evidence in some malignancies that the tumor clone is heterogeneous with respect to proliferation and differentiation. In human leukemia, the tumor clone is organized as a hierar... Most current research on human brain tumors is focused on the molecular and cellular analysis of the bulk tumor mass. However, there is overwhelming evidence in some malignancies that the tumor clone is heterogeneous with respect to proliferation and differentiation. In human leukemia, the tumor clone is organized as a hierarchy that originates from rare leukemic stem cells that possess extensive proliferative and self-renewal potential, and are responsible for maintaining the tumor clone. We report here the identification and purification of a cancer stem cell from human brain tumors of different phenotypes that possesses a marked capacity for proliferation, self-renewal, and differentiation. The increased self-renewal capacity of the brain tumor stem cell (BTSC) was highest from the most aggressive clinical samples of medulloblastoma compared with low-grade gliomas. The BTSC was exclusively isolated with the cell fraction expressing the neural stem cell surface marker CD133. These CD133+ cells could differentiate in culture into tumor cells that phenotypically resembled the tumor from the patient. The identification of a BTSC provides a powerful tool to investigate the tumorigenic process in the central nervous system and to develop therapies targeted to the BTSC. read more read less

Topics:

Cancer stem cell (66%)66% related to the paper, Stem cell (59%)59% related to the paper, Clone (cell biology) (55%)55% related to the paper, Neural stem cell (55%)55% related to the paper, Brain tumor (54%)54% related to the paper
View PDF
4,701 Citations
Author Pic

SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

Get MS-Word and LaTeX output to any Journal within seconds
1
Choose a template
Select a template from a library of 40,000+ templates
2
Import a MS-Word file or start fresh
It takes only few seconds to import
3
View and edit your final output
SciSpace will automatically format your output to meet journal guidelines
4
Submit directly or Download
Submit to journal directly or Download in PDF, MS Word or LaTeX

(Before submission check for plagiarism via Turnitin)

clock Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

''

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

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

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?

Turnitin Stats
Publisher Logos

Freedom from formatting guidelines

One editor, 100K journal formats – world's largest collection of journal templates

With such a huge verified library, what you need is already there.

publisher-logos

Easy support from all your favorite tools

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

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Cancer Research guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Cancer Research citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Cancer Research's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

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 those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo 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.

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

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

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Cancer Research.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Cancer Research Endnote style, according to american-association-for-cancer-research guidelines.

Fast and reliable,
built for complaince.

Instant formatting to 100% publisher guidelines on - SciSpace.

Available only on desktops 🖥

No word template required

Typset automatically formats your research paper to Cancer Research formatting guidelines and citation style.

Verifed journal formats

One editor, 100K journal formats.
With the largest collection of verified journal formats, what you need is already there.

Trusted by academicians

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.

Andreas Frutiger
Researcher & Ex MS Word user
Use this template