Learning Causal Models for Noisy Biological Data Mining: An Application to Ovarian Cancer Detection

Learning Causal Models for Noisy Biological Data Mining: An Application to Ovarian Cancer Detection

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 deepe...

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Bibliographic Details
Main Authors: YAP, Ghim-Eng, TAN, Ah-Hwee, PANG, Hwee Hwa
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2007
Subjects:
Disease detection
Error-handling procedure
Learning causal models
Noisy biological data
Databases and Information Systems
Medicine and Health Sciences
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/394
https://ink.library.smu.edu.sg/context/sis_research/article/1393/viewcontent/Learning_Causal_Models_for_Noisy_Biological_Data_Mining__An_Application_to_Ovarian_Cancer_Detection_edited_.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/394
https://ink.library.smu.edu.sg/context/sis_research/article/1393/viewcontent/Learning_Causal_Models_for_Noisy_Biological_Data_Mining__An_Application_to_Ovarian_Cancer_Detection_edited_.pdf

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