{"type":"ScholarlyArticle","title":"Transfer Learning via Optimal Transportation for Integrative Cancer Patient Stratification","authors":[{"name":"Ziyu Liu","slug":"ziyu-liu","given":"Ziyu","family":"Liu","affiliation":"Department of Statistics , Purdue University"},{"name":"Wei Shao","slug":"wei-shao","given":"Wei","family":"Shao","affiliation":"Biostatistics and Health Data Science , Indiana University School of Medicine"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"Department of Medical and Molecular Genetics , Indiana University School of Medicine"},{"name":"Min Zhang","slug":"min-zhang","given":"Min","family":"Zhang","affiliation":"Department of Statistics , Purdue University"},{"name":"Kun Huang","slug":"kun-huang","given":"Kun","family":"Huang","affiliation":"Biostatistics and Health Data Science , Indiana University School of Medicine; Regenstrief Institute , Indianapolis"}],"abstract":"The Stratification of early-stage cancer patients for the prediction of clinical outcome is a challenging task since cancer is associated with various molecular aberrations. A single biomarker often cannot provide sufficient information to stratify early-stage patients effectively. Understanding the complex mechanism behind cancer development calls for exploiting biomarkers from multiple modalities of data such as histopathology images and genomic data. The integrative analysis of these biomarkers sheds light on cancer diagnosis, subtyping, and prognosis. Another difficulty is that labels for early-stage cancer patients are scarce and not reliable enough for predicting survival times. Given the fact that different cancer types share some commonalities, we explore if the knowledge learned from one cancer type can be utilized to improve prognosis accuracy for another cancer type. We propose a novel unsupervised multi-view transfer learning algorithm to simultaneously analyze multiple biomarkers in different cancer types. We integrate multiple views using non-negative matrix factorization and formulate the transfer learning model based on the Optimal Transport theory to align features of different cancer types. We evaluate the stratification performance on three early-stage cancers from the Cancer Genome Atlas (TCGA) project. Comparing with other benchmark methods, our framework achieves superior accuracy for patient outcome prediction.","doi":"10.24963/ijcai.2021/380","url":"https://ijcai.org/proceedings/2021/380","pdf":"https://ijcai.org/proceedings/2021/0380.pdf","markdown":"https://ijcai.org/proceedings/2021/380.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/380","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":380,"pages":{"first":2760,"last":2766},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning Applications: Bio/Medicine","Machine Learning Applications: Applications of Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Bashiri et al., 2017] Azadeh Bashiri, Marjan Ghazisaeedi, Reza Safdari, Leila Shahmoradi, and Hamide Ehtesham. 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