Predicting tumor malignancies is an important but difficult task. For many tumors, especially neural and endocrine tumors, traditional pathological and histological analyses often can not effectively distinguish benign from malignant tumors. Developing synergistic bioinformatics and computational intelligence system is effective, because deterministic cancer markers do not always exist in individual patients. We proposed a parallel paradigm of cancer and use a number of ensemble methods including boosting, bagging and consensus networking, and have designed a novel classification scheme that advantageously combines several computational intelligence algorithms such as the variants of self-organizing feature map (SOFM) algorithms and the maximum contrast tree (RMCT) algorithms. Boosting and bagging have been advantageously combined. When all of the above are integrated into one synergistic intelligent medical decision system, the prediction power for the task has been significantly boosted. The system and features are validated by diagnosing new patients and by a number of laboratory molecular biology measurements. The outcomes of the research have improved cancer diagnosis and treatment planning, and may lead to diagnose microscopic diseases and better understanding of human genome mechanisms relating to malignant transformation
['Yang J', 'Andrzej Niemierko', 'Mary Qu Yang', 'Zuojie Luo', 'Jianling Li']
Predicting Tumor Malignancies using Combined Computational Intelligence, Bioinformatics and Laboratory Molecular Biology Approaches