2016
Petegrosso, Raphael; Park, Sunho; Hwang, Tae Hyun; Kuang, Rui
Transfer Learning across Ontologies for Phenome-Genome Association Prediction Journal Article
In: Bioinformatics, vol. 33, no. 4, pp. 529-536, 2016.
Abstract | Links | BibTeX | Tags: Phenome-genome Association, Semi-supervised Learning, Transfer Learning
@article{petegrosso2016transfer,
title = {Transfer Learning across Ontologies for Phenome-Genome Association Prediction},
author = {Raphael Petegrosso and Sunho Park and Tae Hyun Hwang and Rui Kuang},
url = {http://bioinformatics.oxfordjournals.org/content/early/2016/10/20/bioinformatics.btw649.abstract},
doi = {10.1093/bioinformatics/btw649},
year = {2016},
date = {2016-11-23},
journal = {Bioinformatics},
volume = {33},
number = {4},
pages = {529-536},
publisher = {Oxford Univ Press},
abstract = {Motivation: To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms.
Results: In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions.
Availability: Source code is available at http://localhost/~raphaelpetegrosso/wpcb/ontophenome.},
keywords = {Phenome-genome Association, Semi-supervised Learning, Transfer Learning},
pubstate = {published},
tppubtype = {article}
}
Motivation: To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms.
Results: In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions.
Availability: Source code is available at http://localhost/~raphaelpetegrosso/wpcb/ontophenome.
Results: In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions.
Availability: Source code is available at http://localhost/~raphaelpetegrosso/wpcb/ontophenome.
2013
Zhang, Huanan; Tian, Ze; Kuang, Rui
Transfer learning across cancers on DNA copy number variation analysis Proceedings Article
In: 2013 IEEE 13th International Conference on Data Mining, pp. 1283–1288, IEEE IEEE, 2013, ISBN: 978-0-7695-5108-1.
Abstract | Links | BibTeX | Tags: Cancer Genomics, DNA Copy Number Variation, Transfer Learning
@inproceedings{zhang2013transfer,
title = {Transfer learning across cancers on DNA copy number variation analysis},
author = {Huanan Zhang and Ze Tian and Rui Kuang},
url = {http://compbio.cs.umn.edu/wp-content/uploads/2017/10/TLFL-10Page.pdf},
doi = {10.1109/ICDM.2013.58},
isbn = {978-0-7695-5108-1},
year = {2013},
date = {2013-12-07},
booktitle = {2013 IEEE 13th International Conference on Data Mining},
pages = {1283--1288},
publisher = {IEEE},
organization = {IEEE},
abstract = {Abstract:
DNA copy number variations (CNVs) are prevalent in all types of tumors. It is still a challenge to study how CNVs play a role in driving tumorgenic mechanisms that are either universal or specific in different cancer types. To address the problem, we introduce a transfer learning framework to discover common CNVs shared across different tumor types as well as CNVs specific to each tumor type from genome-wide CNV data measured by array CGH and SNP genotyping array. The proposed model, namely Transfer Learning with Fused LASSO (TLFL), detects latent CNV components from multiple CNV datasets of different tumor types to distinguish the CNVs that are common across the datasets and those that are specific in each dataset. Both the common and type-specific CNVs are detected as latent components in matrix factorization coupled with fused LASSO on adjacent CNV probe features. TLFL considers the common latent components underlying the multiple datasets to transfer knowledge across different tumor types. In simulations and experiments on real cancer CNV datasets, TLFL detected better latent components that can be used as features to improve classification of patient samples in each individual dataset compared with the model without the knowledge transfer. In cross-dataset analysis on bladder cancer and cross-domain analysis on breast cancer and ovarian cancer, TLFL also learned latent CNV components that are both predictive of tumor stages and correlate with known cancer genes.},
keywords = {Cancer Genomics, DNA Copy Number Variation, Transfer Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Abstract:
DNA copy number variations (CNVs) are prevalent in all types of tumors. It is still a challenge to study how CNVs play a role in driving tumorgenic mechanisms that are either universal or specific in different cancer types. To address the problem, we introduce a transfer learning framework to discover common CNVs shared across different tumor types as well as CNVs specific to each tumor type from genome-wide CNV data measured by array CGH and SNP genotyping array. The proposed model, namely Transfer Learning with Fused LASSO (TLFL), detects latent CNV components from multiple CNV datasets of different tumor types to distinguish the CNVs that are common across the datasets and those that are specific in each dataset. Both the common and type-specific CNVs are detected as latent components in matrix factorization coupled with fused LASSO on adjacent CNV probe features. TLFL considers the common latent components underlying the multiple datasets to transfer knowledge across different tumor types. In simulations and experiments on real cancer CNV datasets, TLFL detected better latent components that can be used as features to improve classification of patient samples in each individual dataset compared with the model without the knowledge transfer. In cross-dataset analysis on bladder cancer and cross-domain analysis on breast cancer and ovarian cancer, TLFL also learned latent CNV components that are both predictive of tumor stages and correlate with known cancer genes.
DNA copy number variations (CNVs) are prevalent in all types of tumors. It is still a challenge to study how CNVs play a role in driving tumorgenic mechanisms that are either universal or specific in different cancer types. To address the problem, we introduce a transfer learning framework to discover common CNVs shared across different tumor types as well as CNVs specific to each tumor type from genome-wide CNV data measured by array CGH and SNP genotyping array. The proposed model, namely Transfer Learning with Fused LASSO (TLFL), detects latent CNV components from multiple CNV datasets of different tumor types to distinguish the CNVs that are common across the datasets and those that are specific in each dataset. Both the common and type-specific CNVs are detected as latent components in matrix factorization coupled with fused LASSO on adjacent CNV probe features. TLFL considers the common latent components underlying the multiple datasets to transfer knowledge across different tumor types. In simulations and experiments on real cancer CNV datasets, TLFL detected better latent components that can be used as features to improve classification of patient samples in each individual dataset compared with the model without the knowledge transfer. In cross-dataset analysis on bladder cancer and cross-domain analysis on breast cancer and ovarian cancer, TLFL also learned latent CNV components that are both predictive of tumor stages and correlate with known cancer genes.