International Journal of Data Mining and Bioinformatics

Aims & Scope

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 in the area of data mining for bioinformatics.

This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	0.4
2024	0.20

SJR (SCImago Journal Rank)

Year	Value
2024	0.130

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	26

Journal Rank

Year	Value
2024	26365

Journal Citation Indicator

Year	Value
2024	27

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Biochemistry, Genetics and Molecular Biology, Computer Science and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Predicting human microRNA-disease associations based on support vector machine

  Citation: 202

  Authors: Qinghua, Guohua, Shuilin, Yu, Yadong

• Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm

  Citation: 79

  Authors: Osama Ahmad, Ahamad Tajudin, Mohammed Azmi Al, Laith Mohammad

• Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra

  Citation: 73

  Authors: Hyun Woo, Seoung Bum, Myong K., Youngja, Nana Gletsu, Thomas R., Dean P.

• The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease

  Citation: 70

  Authors: Massimo, Enzo

• MentalSquares: A generic bipolar Support Vector Machine for psychiatric disorder classification, diagnostic analysis and neurobiological data mining

  Citation: 48

  Authors: Wen Ran, Anand K., Karl E., Yan Qing, Zhongming

• Pupylation sites prediction with ensemble classification model

  Citation: 48

  Authors: Wenzheng, Zhenhua, Chang An, De Shuang

• A novel method to measure the semantic similarity of HPO terms

  Citation: 47

  Authors: Jiajie, Hansheng, Yukai, Xuequn, Yadong, Jin

• Artificial neural network classification of microarray data using new hybrid gene selection method

  Citation: 41

  Authors: Rabia, C.K., Manoj, Namita

• Simulation study in Probabilistic Boolean Network models for genetic regulatory networks

  Citation: 37

  Authors: Shu Qin, Wai Ki, Michael K., Tatsuya

• Integration of multi-omics data for integrative gene regulatory network inference

  Citation: 36

  Authors: Neda, Euiseong, Jung Hun, Sang, Chunyu, Jean, Donghyun, Mingon

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