Dimensionality Reduction based Transfer Learning applied to Pharmacogenomics Databases

• Published in: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2018; pp. 1246 - 1249
• Main Authors: Dhruba, Saugato Rahman, Rahmanl, Raziur, Matlockl, Kevin, Ghosh, Soupatno, Pal, Ranadip
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-07-2018
• Subjects: Drugs; Gene expression; Predictive models; Principal component analysis; Radio frequency; Sensitivity
• ISSN: 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2018.8512457

Recent years have observed a number of Pharmacogenomics databases being published that enable testing of various predictive modeling techniques for personalized therapy applications. However, the consistencies between the databases are usually limited in spite of having significant number of common cell lines and drugs. In this article, we consider the problem of whether we can use the model learned from one secondary database to improve the prediction for the other target database. We illustrate using two pharmacogenomics databases that representing the databases using common basis vectors can improve prediction performance as compared to the naive application of a model trained on one database to another. We also elucidate the robustness of using PCA based basis vectors for scenarios with low correlated input features.