ArticleCancer informatics2007
Data mining for gene networks relevant to poor prognosis in lung cancer via backward-chaining rule induction.
Article in Cancer informatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A data mining based clinical decision support system for survival in lung cancer.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2021Article
- Clinical and molecular models of glioblastoma multiforme survival.International journal of data mining and bioinformatics · 2013Article
- Challenges of the information age: the impact of false discovery on pathway identification.BMC research notes · 2012Article
- Gene expression meta-analysis supports existence of molecular apocrine breast cancer with a role for androgen receptor and implies interactions with ErbB family.BMC medical genomics · 2009Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We use Backward Chaining Rule Induction (BCRI), a novel data mining method for hypothesizing causative mechanisms, to mine lung cancer gene expression array data for mechanisms that could impact survival. Initially, a supervised learning system is used to generate a prediction model in the form of "IF <conditions> THEN <outcome>" style rules. Next, each antecedent (i.e. an IF condition) of a previously discovered rule becomes the outcome class for subsequent application of supervised rule induction. This step is repeated until a termination condition is satisfied. "Chains" of rules are created by working backward from an initial condition (e.g. survival status). Through this iterative process of "backward chaining," BCRI searches for rules that describe plausible gene interactions for subsequent validation. Thus, BCRI is a semi-supervised approach that constrains the search through the vast space of plausible causal mechanisms by using a top-level outcome to kick-start the process. We demonstrate the general BCRI task sequence, how to implement it, the validation process, and how BCRI-rules discovered from lung cancer microarray data can be combined with prior knowledge to generate hypotheses about functional genomics.
Indexed as
Identifiers
19455237PMC2312096What OpenQuestion holds
Registered trials
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