Development of effective tools such as oligo-microarrays and next-generation sequencing methods for monitoring gene expression on a large scale has resulted in the discovery of gene signatures with prognostic/predictive value in various malignant neoplastic diseases. However, with the exponential growth of gene expression databases, biologists are faced with the challenge of extracting useful information from these repositories. Here, we present a software package, BioPlat (Biomarkers Platform), which allows biologists to identify novel prognostic and predictive cancer biomarkers based on the data mining of gene expression signatures and gene expression profiling databases. BioPlat has been designed as an easy-to-use and flexible desktop software application, which provides a set of analytical tools related to data extraction, preprocessing, filtering, gene expression signature calculation, in silico validation, feature selection and annotation that leverage the integration and reuse of gene expression signatures in the context of follow-up data.