International Journal of Data Mining and Bioinformatics
- Editor in Chief
- Prof. Xiaohua (Tony) Hu
- ISSN online
- ISSN print
- 12 issues per year
- Impact factor (Clarivate Analytics) 2017 0.652 (5-Year Impact Factor 0.585)
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.
Topics covered include
- Biological data pre-processing and cleaning
- Biological data visualisation
- Biological data integration and management
- Biomedical ontologies construction/management
- Microarray data analysis
- Protein/RNA structure prediction
- Genomics and proteomics
- Drug design
- Biomedical literature data mining
- Modelling of biomolecular pathways
- Whole, multiple genome comparison
- Systems biology and pathways
- Biological data curation
IJDMB aims to publish the latest research and development results and experiences in the areas of bioinformatics, data mining and knowledge discovery, and the role of data mining techniques and methods in integrating and interpreting the bioinformatics data sets and improving effectiveness and/or efficiency and quality for bioinformatics data analysis. The major objective of IJDMB is to stimulate new multidisciplinary research and the development of cutting-edge data mining methods, techniques and tools to solve problems in bioinformatics. The goal is to help readers understand state-of-the-art techniques/algorithms/methods in bioinformatics data gathering, data pre-processing, data mining and data management.
IJDMB provides a forum to help academics, practitioners, post-graduates and policy makers, working in the area of data mining, data integration and management, bioinformatics, life sciences, healthcare, etc., to disseminate information and to learn from each other's work. The intended audiences are data mining researchers/practitioners; bioinformatics specialists in academia and industry; chemists; system biologists/molecular biologists who rely on computer tools for data integration, data management, data analysis; mathematicians/statisticians who are interested in model development and simulation for life science data; computer scientists; post-graduate students with interests in developing and/or applying novel algorithms/methods in biology/biomedical domains.
IJDMB publishes original research papers (long and short papers, exploratory papers), review papers, technical reports, case studies, conference/workshop reports, application notes, book reviews, commentaries, and news. Special Issues devoted to important topics in data mining and bioinformatics selected from top related conferences such as IEEE CSB, IEEE BIBE, PSB, RECOMB, ISMB, BIOKDD workshop etc., will occasionally be published.
IJDMB is indexed in:
- Journal Citation Reports (Clarivate Analytics)
- Scopus (Elsevier)
- Science Citation Index Expanded (Clarivate Analytics)
- Academic OneFile (Gale)
- ACM Digital Library
- Asian Digital Library
- BIOSIS (Clarivate Analytics)
- cnpLINKer (CNPIEC)
- DBLP Computer Science Bibliography
- Google Scholar
- Info Trac (Gale)
- Inspec (Institution of Engineering and Technology)
- ProQuest Advanced Technologies Database with Aerospace
IJDMB is listed in:More journal lists/directories...
Editor in Chief
- Hu, Xiaohua (Tony), Drexel University, USA
- Chen, Xue-wen, Wayne State University, USA
- Kim, Sun, Seoul National University, South Korea
Editorial Board Members
- Akutsu, Tatsuya, Kyoto University, Japan
- Alhajj, Reda, University of Calgary, Canada
- Aluru, Srinivas, Indian Institute of Technology Bombay and Iowa State University, USA
- Bodenreider, Olivier, U.S. National Library of Medicine, USA
- Chan, Keith C.C., The Hong Kong Polytechnic University, Hong Kong SAR, China
- Chen, Brian, Lehigh University, USA
- Chen, Jake Y., University of Alabama at Birmingham (UAB), USA
- Chen, Luonan, Shanghai Institutes for Biological Sciences, China
- Chen, Yidong, University of Texas Health Science Center at San Antonio, USA
- Cho, Young-Rae, Baylor University, USA
- DasGupta, Bhaskar, University of Illinois at Chicago, USA
- Eisenhaber, Frank, Bioinformatics Institute, Singapore
- Gao, Jean, University of Texas at Arlington, USA
- Ghalwash, Mohamed, Temple University, USA
- Haspel, Nurit, University of Massachusetts at Boston, USA
- He, Jing, Old Dominion University, USA
- Hu, Shuanghua, Bristol-Myers Squibb Company, USA
- Huan, Jun, University of Kansas, USA
- Huang, De-Shuang, Tongji University, China
- Huang, Jingshan, University of South Alabama, USA
- Huang, Yufei, The University of Texas at San Antonio, USA
- Jiang, Xingpeng, Drexel University, USA
- Kwoh, Chee Keong, Nanyang Technological University, Singapore
- Lee, Doheon, KAIST, South Korea
- Li, Guo-Zheng, Tongji University, China
- Li, Xin (James), Georgetown University, USA
- Lin, Hongfei, Dalian University of Technology, China
- Liu, Juan, Wuhan University, China
- Ng, Michael Kwok-Po, Hong Kong Baptist University, Hong Kong SAR, China
- Ng, See-Kiong, Institute for Infocomm Research, Singapore
- Obradovic, Zoran, Temple University, USA
- Park, Taesung, Seoul National University, South Korea
- Policriti, Alberto, Università di Udine, Italy
- Qiu, Robin G., The Pennsylvania State University, USA
- Rebholz-Schuhmann, Dietrich, European Bioinformatics Institute, UK
- Ressom, Habtom, Georgetown University, USA
- Rombo, Simona E., Università degli Studi di Palermo, Italy
- Tian, Tianhai, Monash University, Australia
- Tseng, Vincent Shin-Mu, National Cheng Kung University, Taiwan
- Tsui, Stephen Kwok-Wing, Chinese University of Hong Kong, Hong Kong SAR, China
- Vingron, Martin, Max Planck Institute for Molecular Genetics, Germany
- Wang, James (Zijun), Clemson University, USA
- Wang, Jason T. L., New Jersey Institute of Technology, USA
- Wang, Jianxin, Central South University, China
- Wang, Xun, Zhejiang Gongshang University, China
- Wang, Yu-Ping, Tulane University, USA
- Wu, Fang Xiang, University of Saskatchewan, Canada
- Xu, Dong, University of Missouri, USA
- Xu, Hua, Vanderbilt University Medical Center, USA
- Yoo, Illhoi, University of Missouri, USA
- Zhang, Aidong, University at Buffalo, The State University of New York, USA
- Zhang, Puwen Peter, Wyeth Research, USA
- Zheng, Huiru (Jane), University of Ulster, UK
- Zhou, Yanhong, Huazhong University of Science and Technology, China
- Zhu, Xiaofeng, Guangxi Normal University, China
- Zou, Xiufen, Wuhan University, China
A few essentials for publishing in this journal
- Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
- Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
- Briefs and research notes are not published in this journal.
- All our articles go through a double-blind review process.
- All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
- There are no charges for publishing with Inderscience, unless you require your article to be Open Access (OA). You can find more information on OA here.
- All articles for this journal must be submitted using our online submissions system.
- Submit here.
Unzip your genes
7 September, 2018
A research team in China is developing a new genotyping method using deletion visualisation and classification. This looks at where parts of genes have been lost during DNA repair after damage. Their results showed that the approach was more accurate than earlier methods, had a wider detectable deletion length range, and was able to perform better with high and low coverage data. Tests on simulated data from a range of diseases with high levels of noise compared well against genotype "calling" methods such as Pindel and LUMPY (a probabilistic framework) [...]More details...