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http://hdl.handle.net/1942/30062
Title: | High dimensional surrogacy: computational aspects of an upscaled analysis | Authors: | SENGUPTA, Rudradev PERUALILA, Nolen Joy SHKEDY, Ziv Biecek, Przemyslaw MOLENBERGHS, Geert BIJNENS, Luc |
Issue Date: | 2020 | Publisher: | TAYLOR & FRANCIS INC | Source: | Journal of Biopharmaceutical Statistics, 30 (1), p. 104-120 | Abstract: | Identification of genomic biomarkers is an important area of research in the context of drug discovery experiments. These experiments typically consist of several high dimensional datasets that contain information about a set of drugs (compounds) under development. This type of data structure introduces the challenge of multi-source data integration. High-Performance Computing (HPC) has become an important tool for everyday research tasks. In the context of drug discovery, high dimensional multi-source data needs to be analyzed to identify the biological pathways related to the new set of drugs under development. In order to process all information contained in the datasets, HPC techniques are required. Even though R packages for parallel computing are available, they are not optimized for a specific setting and data structure. In this article, we propose a new framework, for data analysis, to use R in a computer cluster. The proposed data analysis workflow is applied to a multi-source high dimensional drug discovery dataset and compared with a few existing R packages for parallel computing. | Notes: | [Sengupta, Rudradev; Shkedy, Ziv; Molenberghs, Geert; Bijnens, Luc] Hasselt Univ, Ctr Stat CenStat, Hasselt, Belgium. [Sengupta, Rudradev; Bijnens, Luc] Janssen Pharmaceut Co Johnson & Johnson, Nonclin Stat, Beerse, Belgium. [Perualila, Nolen Joy] Janssen Pharmaceut Co Johnson & Johnson, HEMAR EMEA, Beerse, Belgium. [Shkedy, Ziv; Molenberghs, Geert] Interuniv Inst Biostat & Stat Bioinformat I BioSt, Hasselt, Belgium. [Biecek, Przemyslaw] Warsaw Univ Technol, Fac Math & Informat Sci, Warsaw, Poland. | Keywords: | Integrated data analysis;joint modeling;drug discovery;big and high dimensional data;computational optimization;fingerprint feature | Document URI: | http://hdl.handle.net/1942/30062 | ISSN: | 1054-3406 | e-ISSN: | 1520-5711 | DOI: | 10.1080/10543406.2019.1657128 | ISI #: | 000484249700001 | Rights: | 2019 Taylor & Francis Group, LLC | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2020 |
Appears in Collections: | Research publications |
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