Publication Type

Conference Proceeding Article


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Undetected errors in the expression measurements from highthroughput DNA microarrays and protein spectroscopy could seriously affect the diagnostic reliability in disease detection. In addition to a high resilience against such errors, diagnostic models need to be more comprehensible so that a deeper understanding of the causal interactions among biological entities like genes and proteins may be possible. In this paper, we introduce a robust knowledge discovery approach that addresses these challenges. First, the causal interactions among the genes and proteins in the noisy expression data are discovered automatically through Bayesian network learning. Then, the diagnosis of a disease based on the network is performed using a novel error-handling procedure, which automatically identifies the noisy measurements and accounts for their uncertainties during diagnosis. An application to the problem of ovarian cancer detection shows that the approach effectively discovers causal interactions among cancer-specific proteins. With the proposed error-handling procedure, the network perfectly distinguishes between the cancer and normal patients.


Disease detection, Error-handling procedure, Learning causal models, Noisy biological data


Databases and Information Systems | Medicine and Health Sciences | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics


Proceedings of the Twenty-second AAAI Conference on Artificial Intelligence: 22-26 July 2007, Vancouver, British Columbia, Canada

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AAAI Press

City or Country

Menlo Park, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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