Example of International Journal of Data Mining and Bioinformatics format
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Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format
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Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format Example of International Journal of Data Mining and Bioinformatics format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

International Journal of Data Mining and Bioinformatics — Template for authors

Categories Rank Trend in last 3 yrs
Library and Information Sciences #69 of 235 up up by 9 ranks
Biochemistry, Genetics and Molecular Biology (all) #122 of 204 up up by 8 ranks
Information Systems #199 of 329 down down by 18 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 163 Published Papers | 305 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 10/07/2020
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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.

0.772

2% from 2018

Impact factor for International Journal of Data Mining and Bioinformatics from 2016 - 2019
Year Value
2019 0.772
2018 0.789
2017 0.652
2016 0.624
graph view Graph view
table view Table view

1.9

6% from 2019

CiteRatio for International Journal of Data Mining and Bioinformatics from 2016 - 2020
Year Value
2020 1.9
2019 1.8
2018 1.5
2017 1.2
2016 1.1
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 2% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 6% 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.

0.214

33% from 2019

SJR for International Journal of Data Mining and Bioinformatics from 2016 - 2020
Year Value
2020 0.214
2019 0.321
2018 0.237
2017 0.217
2016 0.319
graph view Graph view
table view Table view

0.465

19% from 2019

SNIP for International Journal of Data Mining and Bioinformatics from 2016 - 2020
Year Value
2020 0.465
2019 0.572
2018 0.347
2017 0.344
2016 0.367
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 33% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 19% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

International Journal of Data Mining and Bioinformatics

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Inderscience Publishers

International Journal of Data Mining and Bioinformatics

Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies i...... Read More

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Last updated on
10 Jul 2020
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ISSN
1748-5673
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Impact Factor
Medium - 0.846
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Open Access
No
i
Sherpa RoMEO Archiving Policy
Yellow faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
plainnat
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Citation Type
Author Year
(Blonder et al., 1982)
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Bibliography Example
Beenakker, C. W. J. (2006). ‘Specular Andreev Reflection in Graphene’. Phys. Rev. Lett., Vol 97, No 6, pp. 067007.

Top papers written in this journal

Journal Article DOI: 10.1504/IJDMB.2017.10009480
Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm
Osama Ahmad Alomari1, Ahamad Tajudin Khader1, Mohammed Azmi Al-Betar2, Laith Abualigah1

Abstract:

In this paper, the bat-inspired algorithm (BA) is tolerated to gene selection for cancer classification using microarray datasets. Microarray data consists of irrelevant, redundant, and noisy genes. Gene selection problem is tackled by determining the most informative genes taken from microarray data to accurately diagnose th... In this paper, the bat-inspired algorithm (BA) is tolerated to gene selection for cancer classification using microarray datasets. Microarray data consists of irrelevant, redundant, and noisy genes. Gene selection problem is tackled by determining the most informative genes taken from microarray data to accurately diagnose the cancer disease. Gene selection problem is widely solved by optimisation algorithms. BA is a recent swarm-based algorithm, which imitates the echolocation system of bat individuals. It has been successfully applied to several optimisation problems. Gene selection is tackled by combining two stages, namely, filter stage, which uses Minimum Redundancy Maximum Relevancy (MRMR) method; and wrapper stage, which uses BA and SVM. To test the accuracy performance of the proposed method, ten microarray datasets were used. For comparative evaluation, the proposed method was compared with popular gene selection methods. The proposed method achieves comparable results of some datasets and produced new results for one dataset. read more read less
53 Citations
Journal Article DOI: 10.1504/IJDMB.2017.084268
A novel method to measure the semantic similarity of HPO terms
Jiajie Peng1, Hansheng Xue2, Yukai Shao2, Xuequn Shang1, Yadong Wang2, Jin Chen3

Abstract:

It is critical yet remains to be challenging to make precise disease diagnosis from complex clinical features and highly heterogeneous genetic background. Recently, phenotype similarity has been effectively applied to model patient phenotype data. However, the existing measurements are revised based on the Gene Ontology-based... It is critical yet remains to be challenging to make precise disease diagnosis from complex clinical features and highly heterogeneous genetic background. Recently, phenotype similarity has been effectively applied to model patient phenotype data. However, the existing measurements are revised based on the Gene Ontology-based term similarity models, which are not optimised for human phenotype ontologies. We propose a new similarity measure called PhenoSim. Our model includes a noise reduction component to model the noisy patient phenotype data, and a path-constrained Information Content-based method for phenotype semantics similarity measurement. Evaluation tests compared PhenoSim with four existing approaches. It showed that PhenoSim, could effectively improve the performance of HPO-based phenotype similarity measurement, thus increasing the accuracy of phenotype-based causative gene prediction and disease prediction. read more read less

Topics:

Semantic similarity (61%)61% related to the paper, Similarity measure (60%)60% related to the paper, Similarity (network science) (56%)56% related to the paper
45 Citations
Journal Article DOI: 10.1504/IJDMB.2017.084026
Artificial neural network classification of microarray data using new hybrid gene selection method
Rabia Aziz1, C. K. Verma1, Manoj Jha1, Namita Srivastava1

Abstract:

This paper proposed a new combination of feature selection/extraction approach for Artificial Neural Networks (ANNs) classification of high-dimensional microarray data, which uses an Independent Component Analysis (ICA) as an extraction technique and Artificial Bee Colony (ABC) as an optimisation technique. The study evaluate... This paper proposed a new combination of feature selection/extraction approach for Artificial Neural Networks (ANNs) classification of high-dimensional microarray data, which uses an Independent Component Analysis (ICA) as an extraction technique and Artificial Bee Colony (ABC) as an optimisation technique. The study evaluates the performance of the proposed ICA + ABC algorithm by conducting extensive experiments on five-binary and one multi-class gene expression microarray data set and compared the proposed algorithm with ICA and ABC. The proposed method shows superior performance as it achieves the highest classification accuracy along with the lowest average number of selected genes. Furthermore, the present work compares the proposed ICA + ABC algorithm with popular filter techniques and with other similar bio-inspired algorithms with ICA. The experimental results show that the proposed algorithm gives more accurate classification rate for ANN classifier. Therefore, ICA + ABC are a promising approach for solving gene selection and cancer classification problems using microarray data. read more read less

Topics:

Feature selection (52%)52% related to the paper
42 Citations
Journal Article DOI: 10.1504/IJDMB.2017.10007470
Pupylation sites prediction with ensemble classification model
Wenzheng Bao1, Zhenhua Huang1, Chang An Yuan, De-Shuang Huang1

Abstract:

Post-translational modification of protein is one of the most important biological processions in the field of proteomics and bioinformatics. Pupylation is a novel post translational modification which the small, intrinsically disordered prokaryotic ubiquitin-like protein is conjugated to lysine residues of potential segments... Post-translational modification of protein is one of the most important biological processions in the field of proteomics and bioinformatics. Pupylation is a novel post translational modification which the small, intrinsically disordered prokaryotic ubiquitin-like protein is conjugated to lysine residues of potential segments. Both the experimental and computational prediction methods of such modified sites have proved to be a challenging issue. Computational methods mainly aimed at extracting effective features from the potential protein segments. In this paper, the statistical feature of adjacent amino acid residues has been proposed and the novel feature is combined appearance of adjacent amino acid and the BLOSUM62 matrix. The Neural Network and the Naive Bayesian model have been employed as the classification model in this work. Such model will also be utilised to deal with many other issues in the field of computational biology. read more read less
39 Citations
Journal Article DOI: 10.1504/IJDMB.2017.084027
A novel point density based validity index for clustering gene expression datasets
M. Arif Wani1, Romana Riyaz1

Abstract:

Elucidating the patterns hidden in gene expression data offers an opportunity for identifying co-expressed genes and biologically relevant grouping of genes. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the microarray data. A fir... Elucidating the patterns hidden in gene expression data offers an opportunity for identifying co-expressed genes and biologically relevant grouping of genes. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the microarray data. A first step toward addressing this challenge is the use of clustering techniques. Validation of results obtained from a clustering algorithm is an important part of the clustering process. In this paper, we propose a new cluster validity index (ARPoints index) for the purpose of cluster validation. A new approach to determine the compactness measure and distinctness measure of clusters is presented. We revisit commonly known indices and conduct a thorough comparison of these indices with the proposed index and provide a summary of performance evaluation of different indices. Experimental results show that the proposed index performs better than the commonly known cluster validity indices. read more read less

Topics:

Fuzzy clustering (61%)61% related to the paper, Cluster analysis (61%)61% related to the paper, Correlation clustering (61%)61% related to the paper, Clustering high-dimensional data (60%)60% related to the paper, CURE data clustering algorithm (59%)59% related to the paper
21 Citations
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Frequently asked questions

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3. Can I cite my article in multiple styles in International Journal of Data Mining 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 International Journal of Data Mining and Bioinformatics citation style.

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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 International Journal of Data Mining and Bioinformatics that you can download at the end.

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12. Is International Journal of Data Mining and Bioinformatics's impact factor high enough that I should try publishing my article there?

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13. What is Sherpa RoMEO Archiving Policy for International Journal of Data Mining 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 International Journal of Data Mining 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 International Journal of Data Mining and Bioinformatics?

The 5 most common citation types in order of usage for International Journal of Data Mining and Bioinformatics are:.

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

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16. Can I download International Journal of Data Mining 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 International Journal of Data Mining and Bioinformatics Endnote style according to Elsevier guidelines.

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