Multisource data Inference on multi-source data

Intro

1. A review on machine learning principles for multi-view biological data integration

   • 발표자 : 한상일 발표자료

2. Statistical methods in integrative genomics

   • 발표자 : 김민우 발표자료

3. General overview on the merits of multimodal neuroimaging data fusion

   • 발표자 : 박재성 발표자료

4. Multiple factor analysis: principal component analysis for multitable and multiblock data sets

   • 발표자 : 장우녕 발표자료

Multi source

5. Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

   • 발표자 : 김재민 발표자료

6. Structural learning and integrative decomposition of multi-view data

   • 발표자 : 최서원 발표자료

7. A general framework for association analysis of heterogeneous data

   • 발표자 : 이수진 발표자료

8. Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank Models

   • 발표자 : 조동혁 발표자료

9. Supervised multiway factorization

   • 발표자 : 박재성 발표자료

10. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

    • 발표자 : 김민우 발표자료

11. Integrating multi-source block-wise missing data in model selection

    • 발표자 : 장우녕 발표자료

12. Joint and individual analysis of breast cancer histologic images and genomic covariates

    • 발표자 : 장우녕 발표자료

13. D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets

    • 발표자 : 조동혁 발표자료

14. Integrative Factorization of Bidimensionally Linked Matrices

    • 발표자 : 홍승기 발표자료

15. Incorporating biological information in sparse principal component analysis with application to genomic data

    • 발표자 : 박재성 발표자료

16. OnPLS—A Novel Multiblock Method for the Modelling of Predictive and Orthogonal Variation

    • 발표자 : 한상일 발표자료