Forthcoming articles

International Journal of Data Mining and Bioinformatics

International Journal of Data Mining and Bioinformatics (IJDMB)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Data Mining and Bioinformatics (2 papers in press)

Regular Issues

  • An Integrated Approach for DNA-Damage Detection from Comet-Images of Drosophila Melanogaster   Order a copy of this article
    by Mukerrem Bahar Baskir, Fahriye Zemheri Navruz 
    Abstract: Image processing is a popular technique in data mining. Researchers can obtain various results from an image related to experimental study using this technique. In this study, we proposed an approach to make inference from comet assay images used for identification of genotoxins causing several disorders in chromosome and DNA structure. This proposed approach has three phases: i) Creating comet assay images after giving mineral oil (1.19 ?l/L) for 24-, 48- and 72-hours as diet to Drosophila melanogaster known as in vivo model organism. ii) Transforming these comet images into quantitative images using texture analysis in image processing, iii) Clustering the quantitative images in order to detect DNA damages in comet images by similarities of 24-, 48-, 72-hourly experiments and control group. The accuracy rate of clustering analysis is 95%. Consequently, this proposed approach reveals convenient and precise results for the detection of DNA damage in Drosophila Melanogaster.
    Keywords: Image processing; comet assay; texture; clustering; accuracy; Drosophila Melanogaster.

  • Gradient Boosting Tree for 1H-MRS Alzheimer Diagnosis   Order a copy of this article
    by Defu Liu, Guowu Yang, Fengmao Lv, Yuchen Li, Jinzhao Wu 
    Abstract: In recent years, increasing attention is drawn to early-onset alzheimer\'s disease (EOAD). As effective biomarkers for EOAD, the brain metabolites, measured by proton magnetic resonance spectroscopy (1H-MRS), are significantly sensitive to the brain metabolite changes in dementia patients. This work aims to design an effective EOAD computer-aided system through mining the 1H-MRS data with advanced machine learning techniques. Specifically, our method first adopts gradient boosting decision tree (GBDT) to learn the 1H-MRS biomarkers of EOAD patients, which are then used to construct the final classifier for Alzheimer diagnosis. To validate our proposal, we have conducted comprehensive experiments for evaluation and the experimental results clearly demonstrate the effectiveness of our method.
    Keywords: Early-onset Alzheimer\'s Disease; Proton Magnetic Resonance Spectroscopy; Alzheimer Biomarker; Gradient Boosting Decision Tree.

 

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