Abstract
We propose a methodology for predictive classification from gene expression data, able to combine the robustness of high-dimensional statistical classification methods with the comprehensibility and interpretability of simple logic-based models. We first construct a robust classifier combining contributions of a large number of gene expression values, and then (meta)-mine the classifier for compact summarizations of subgroups among genes associated with a given class therein. The subgroups are described by means of relational logic features extracted from publicly available gene ontology information. The curse of dimensionality pertaining to the gene expression based classification problem due to the large number of attributes (genes) is turned into an advantage in the secondary, meta-mining task as here the original attributes become learning examples. We cross-validate the proposed method on two classification problems: (i) acute lymphoblastic leukemia (ALL) vs. acute myeloid leukemia (AML), (ii) seven subclasses of ALL.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the Fourth IASTED International Conference on Biomedical Engineering |
| Pages | 19-24 |
| Number of pages | 6 |
| Volume | 2006 |
| State | Published - Dec 1 2006 |
| Event | 4th IASTED International Conference on Biomedical Engineering - Innsbruck, Austria Duration: Feb 15 2006 → Feb 17 2006 |
Other
| Other | 4th IASTED International Conference on Biomedical Engineering |
|---|---|
| Country | Austria |
| City | Innsbruck |
| Period | 2/15/06 → 2/17/06 |
Fingerprint
Keywords
- Gene expression microarrays
- Gene ontology
- Relational data mining
Cite this
Summarizing gene-expression-based classifiers by meta-mining comprehensible relational patterns. / Železný, Filip; Štěpánková, Olga; Tolar, Jakub; Lavrač, Nada.
Proceedings of the Fourth IASTED International Conference on Biomedical Engineering. Vol. 2006 2006. p. 19-24.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Summarizing gene-expression-based classifiers by meta-mining comprehensible relational patterns
AU - Železný, Filip
AU - Štěpánková, Olga
AU - Tolar, Jakub
AU - Lavrač, Nada
PY - 2006/12/1
Y1 - 2006/12/1
N2 - We propose a methodology for predictive classification from gene expression data, able to combine the robustness of high-dimensional statistical classification methods with the comprehensibility and interpretability of simple logic-based models. We first construct a robust classifier combining contributions of a large number of gene expression values, and then (meta)-mine the classifier for compact summarizations of subgroups among genes associated with a given class therein. The subgroups are described by means of relational logic features extracted from publicly available gene ontology information. The curse of dimensionality pertaining to the gene expression based classification problem due to the large number of attributes (genes) is turned into an advantage in the secondary, meta-mining task as here the original attributes become learning examples. We cross-validate the proposed method on two classification problems: (i) acute lymphoblastic leukemia (ALL) vs. acute myeloid leukemia (AML), (ii) seven subclasses of ALL.
AB - We propose a methodology for predictive classification from gene expression data, able to combine the robustness of high-dimensional statistical classification methods with the comprehensibility and interpretability of simple logic-based models. We first construct a robust classifier combining contributions of a large number of gene expression values, and then (meta)-mine the classifier for compact summarizations of subgroups among genes associated with a given class therein. The subgroups are described by means of relational logic features extracted from publicly available gene ontology information. The curse of dimensionality pertaining to the gene expression based classification problem due to the large number of attributes (genes) is turned into an advantage in the secondary, meta-mining task as here the original attributes become learning examples. We cross-validate the proposed method on two classification problems: (i) acute lymphoblastic leukemia (ALL) vs. acute myeloid leukemia (AML), (ii) seven subclasses of ALL.
KW - Gene expression microarrays
KW - Gene ontology
KW - Relational data mining
UR - http://www.scopus.com/inward/record.url?scp=33847176643&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847176643&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0889865787
SN - 9780889865785
VL - 2006
SP - 19
EP - 24
BT - Proceedings of the Fourth IASTED International Conference on Biomedical Engineering
ER -