Data Mining Research Lab, Kno.e.sis Center of Excellence, Department of Computer Science and Engineering Wright State University, USA
Received date: February 08, 2014; Accepted date: February 11, 2014; Published date: February 14, 2014
Citation: Dong G (2014) Editorial for International Journal of Biomedical Data Mining. Int J Biomed Data Min 3:e101. doi: 10.4172/2090-4924.1000e101
Copyright: © 2014 Dong G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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This issue of the International Journal of Biomedical Data Mining presents two contributed articles. The first article, entitled Data Inventory for Cancer Patients Receiving Radiotherapy for Outcome Analysis and Modeling, authored by Jason Vickress, Rob Barnett and Slav Yartsev, describes a database created for storing and analyzing patient specific data related to pre-treatment condition, treatment planning, and treatment outcomes, for patients receiving radiotherapy based cancer treatment. The proposed database can perform automated analysis regarding quality assurance, dose accumulation for multiple treatments on different machines and can assist physicians in choosing the optimal radiation therapy for new patients. The second article, entitled Likelihood Ratio Test of Hardy-Weinberg Equilibrium Using Uncertain Genotypes for Sibship Data, authored by Qiong Li, Helene Massam and Xin Gao, is concerned with the problem of testing for Hardy-Weinberg equilibrium of genotype frequencies in the area of population genetics. This paper develops an Expectation-Maximization algorithm to estimate the genotype frequencies for sibship data with genotype uncertainty, and develops a likelihood ratio test of Hardy- Weinberg equilibrium for sibships where parental genotypes are not available and where genotyping errors may exist.
The International Journal of Biomedical Data Mining is a scholarly open access, peer-reviewed, and fully refereed journal which publishes original research papers on valuable algorithms, methods and software tools in the fields of data mining, knowledge discovery, data analysis and machine learning, and their application to compelling biomedical, healthcare and bioinformatics problems. Contributions will come from disciplines such as computer science, engineering, statistics, biomedical informatics, science and mathematics. Papers will present original research in the field, highlighting methodological aspects and providing experimental evidence of their effectiveness on specific problems and all aspects of data mining applied to high-dimensional biological and biomedical data. 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 multidisciplinary research area. Comprehensive review articles, short papers and book and software reviews are also welcome.