BACKGROUND: The high growth of Next Generation Sequencing data currently demands 
new knowledge extraction methods. In particular, the RNA sequencing gene 
expression experimental technique stands out for case-control studies on cancer, 
which can be addressed with supervised machine learning techniques able to 
extract human interpretable models composed of genes, and their relation to the 
investigated disease. State of the art rule-based classifiers are designed to 
extract a single classification model, possibly composed of few relevant genes. 
Conversely, we aim to create a large knowledge base composed of many rule-based 
models, and thus determine which genes could be potentially involved in the 
analyzed tumor. This comprehensive and open access knowledge base is required to 
disseminate novel insights about cancer.
RESULTS: We propose CamurWeb, a new method and web-based software that is able 
to extract multiple and equivalent classification models in form of logic 
formulas ("if then" rules) and to create a knowledge base of these rules that 
can be queried and analyzed. The method is based on an iterative classification 
procedure and an adaptive feature elimination technique that enables the 
computation of many rule-based models related to the cancer under study. 
Additionally, CamurWeb includes a user friendly interface for running the 
software, querying the results, and managing the performed experiments. The user 
can create her profile, upload her gene expression data, run the classification 
analyses, and interpret the results with predefined queries. In order to 
validate the software we apply it to all public available RNA sequencing 
datasets from The Cancer Genome Atlas database obtaining a large open access 
knowledge base about cancer. CamurWeb is available at 
http://bioinformatics.iasi.cnr.it/camurweb .
CONCLUSIONS: The experiments prove the validity of CamurWeb, obtaining many 
classification models and thus several genes that are associated to 21 different 
cancer types. Finally, the comprehensive knowledge base about cancer and the 
software tool are released online; interested researchers have free access to 
them for further studies and to design biological experiments in cancer 
research.