Identifying Cancer Biomarkers Via Node Classification within a Mapreduce Framework

Big data are giving new research challenges in the life sciences domain because of their variety, volume, veracity, velocity, and value. Predicting gene biomarkers is one of the vital research issues in bioinformatics field, where microarray gene expression and network based methods can be used. These datasets suffer from the huge data voluminous, causing main memory problems. In this paper, a Random Committee Node Classifier algorithm (RCNC) is proposed for identifying cancer biomarkers, which is based on microarray gene expression data and Protein-Protein Interaction (PPI) data. Data are enriched from other public databases, such as IntACT1 and UniProt2 and Gene Ontology3 (GO). Cancer Biomarkers are identified when applied to different datasets with an accuracy rate an accuracy rate 99.16%, 99.96% precision, 99.24% recall, 99.16% F1-measure and 99.6 ROC. To speed up the performance, it is run within a MapReduce framework, where RCNC MapReduce algorithm is much faster than RCNC sequential algorithm when having large datasets.

@article{Soliman2015,
  title     = {Identifying Cancer Biomarkers Via Node Classification within a Mapreduce Framework},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {12},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Taysir Hassan A. Soliman},
  doi       = {10.14569/IJACSA.2015.061225},
  url       = {https://doi.org/10.14569/IJACSA.2015.061225}
}