IEEE Transactions on Knowledge and Data Engineering
A New Uncertainty Measure for Belief Networks with Applications to Optimal Evidential Inferencing
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A novel network model for molecular prognosis
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
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Objective: Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers. Methods and materials: An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: (1) identifying genes significantly associated with lung cancer survival; (2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; (3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; (4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and (5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks. Results: A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P