Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers
| dc.contributor.author | Gao, Katie | |
| dc.contributor.author | Wang, Dayong | |
| dc.contributor.author | Huang, Yi | |
| dc.date.accessioned | 2018-11-19T15:46:26Z | |
| dc.date.available | 2018-11-19T15:46:26Z | |
| dc.date.issued | 2018-10-22 | |
| dc.description.abstract | Conventional cancer drug development has long been limited to organ- or tissue-specific cancer types. However, it has become increasingly known that specific genetic abnormalities are responsible for the carcinogenesis of multiple cancers. The recent US Food and Drug Administration (FDA) approval of the first multi-cancer drug, Keytruda, has demonstrated the feasibility of developing new drugs that target multiple cancers. Despite a promising future, methodological development for identifying multi-cancer molecular targets remains encumbered. This study developed a novel machine learning approach to identify such genes responsible for multiple cancers by synthesizing salient genomic information from cancer-specific classification models. This approach centered on the cross-cancer prediction method for identifying groups of cancers with high cross-cancer predictability. Furthermore, a robust hybrid classifier, comprising Prediction Analysis for Microarrays and Random Forest, was developed to integrate predictive models for gene inference. This approach has successfully identified key genes shared by endometrial cancer, mammary gland ductal carcinoma, and small cell lung cancer. The results are supported by published experimental evidence. This framework holds potential to transform the current methods of discovering multi-cancer molecular targets for clinical oncology. | en_US |
| dc.description.uri | http://journals.sagepub.com/doi/full/10.1177/1176935118805398 | en_US |
| dc.format.extent | 8 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/M2SN0180B | |
| dc.identifier.citation | Katie Gao, Dayong Wang, Yi Huang, Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers, Cancer Informatics Volume 17: 1–8, 2018, https://doi.org/10.1177/1176935118805398 | en_US |
| dc.identifier.uri | https://doi.org/10.1177/1176935118805398 | |
| dc.identifier.uri | http://hdl.handle.net/11603/12050 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | SAGE journals | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mathematics Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.subject | cross-cancer prediction | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | molecular target | en_US |
| dc.subject | multi-cancer | en_US |
| dc.title | Cross-cancer Prediction: A Novel Machine Learning Approach to Discover Molecular Targets for Development of Treatments for Multiple Cancers | en_US |
| dc.type | Text | en_US |