Lung tumor diagnosis and subtype discovery by gene expression profiling.
The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously difficult because of the limitations in clinical presentation and histopathology. To accurate diagnose complex diseases, the molecular classification based on gene or protein expression profiles are indispensable for modern medicine. Moreover, many heterogeneous diseases consist of various potential subtypes in molecular basis and differ remarkably in their response to therapies. It is critical to accurate predict subgroup on disease gene expression profiles. More fundamental knowledge of the molecular basis and classification of disease could aid in the prediction of patient outcome, the informed selection of therapies, and identification of novel molecular targets for therapy. In this paper, we propose a new disease diagnostic method, probabilistic boosting tree (PB tree) method, on gene expression profiles of lung tumors. It enables accurate disease classification and subtype discovery in disease. It automatically constructs a tree in which each node combines a number of weak classifiers into a strong classifier. Also, subtype discovery is naturally embedded in the learning process. Our algorithm achieves excellent diagnostic performance, and meanwhile it is capable of detecting the disease subtype based on gene expression profile.
Wang
Lu-yong
LY
Integrated Data Syst. Dept., Siemens Corp. Res., Princeton, NJ 08540, USA. luyong.wang@siemens.com
Tu
Zhuowen
Z
D000230:Adenocarcinoma / Q000175:diagnosis / Q000235:genetics; D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D030342:Genetic Diseases, Inborn / Q000235:genetics; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000235:genetics; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011336:Probability