The microarray technology used in biological and medical research provides a new idea for the diagnosis and treatment of cancer. To find different types of cancer and to classify the cancer samples accurately, we propose a new cluster ensemble framework Dual Neural Gas Cluster Ensemble (DNGCE), which is based on neural gas algorithm, to discover the underlying structure of noisy cancer gene expression profiles. This framework DNGCE applies the neural gas algorithm to perform clustering not only on the sample dimension, but also on the attribute dimension. It also adopts the normalized cut algorithm to partition off the consensus matrix constructed from multiple clustering solutions. We obtained the final accurate results. Experiments on cancer gene expression profiles illustrated that the proposed approach could achieve good performance, as it outperforms the single clustering algorithms and most of the existing approaches in the process of clustering gene expression profiles.
Magnetic induction hyperthermia becomes a very important tumor treatment method at present. In order to ensure a successful operation, doctors should make hyperthermia treatment planning before surgery. Based on Integration Healthcare Enterprise (IHE) framework and Digital Imaging and Communications in Medcine (DICOM) standard, we proposed and carried out a network workflow integrated with modern medical information systems for the dissemination of information in magnetic induction hyperthermia like accurate accessing patient information and radiology image data, storing processed images, sharing and verifying hyperthermia reports. The results proved that our system could not only improve the efficiency of magnetic induction hyperthermia treatment planning, but also save medical resources and reduce labor costs.