Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies

Authors:
Philip Ganchev;David Malehorn;William L. Bigbee;Vanathi Gopalakrishnan
Affiliations:
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States;Department of Pathology, University of Pittsburgh, Pittsburgh, PA, United States;Department of Pathology, University of Pittsburgh, Pittsburgh, PA, United States;Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States and Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
Venue:
Journal of Biomedical Informatics
Year:
2011

Citing 10
Cited 2

Multitask Learning

Machine Learning - Special issue on inductive transfer
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity

Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Improving SVM accuracy by training on auxiliary data sources

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models

Bioinformatics
EPO-KB

Bioinformatics
Domain adaptation with structural correspondence learning

EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Bayesian rule learning for biomedical data mining

Bioinformatics
A bayesian rule generation framework for 'omic' biomedical data analysis

A bayesian rule generation framework for 'omic' biomedical data analysis
A Survey on Transfer Learning

IEEE Transactions on Knowledge and Data Engineering
Rule learning for disease-specific biomarker discovery from clinical proteomic mass spectra

BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications

Editorial: Selected Papers from the 2011 Summit on Translational Bioinformatics

Journal of Biomedical Informatics
Multi model transfer learning with RULES family

MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel framework for integrative biomarker discovery from related but separate data sets created in biomarker profiling studies. The framework takes prior knowledge in the form of interpretable, modular rules, and uses them during the learning of rules on a new data set. The framework consists of two methods of transfer of knowledge from source to target data: transfer of whole rules and transfer of rule structures. We evaluated the methods on three pairs of data sets: one genomic and two proteomic. We used standard measures of classification performance and three novel measures of amount of transfer. Preliminary evaluation shows that whole-rule transfer improves classification performance over using the target data alone, especially when there is more source data than target data. It also improves performance over using the union of the data sets.