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A Multi-Modular System-Genetics (MMSG) Approach for Deep Representation Learning for Personalized Treatment of Cancer Using Sensitivity Analysis of Precision Drugs and Gene Expression Data

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Data-Enabled Discovery and Applications

Abstract

One-size-fits-all has never succeeded profoundly and this is the reason why personalization has become so important. Many fatal diseases face such problem due to intense and risky medication procedures with immense side effects. Personalized medication through precision medicine is the new big thing that will provide better treatment solution through analysis and artificial intelligence. In this work, we have introduced for the first time a multi-modular training approach (MMSG+DRRL/ERRL) for generalized representation for cancer drug prediction based on gene expression for personalized treatment through the use of different system-genetics information like subclinical medical information to determine their suitability. We have shown how different modular information can be used computationally to enhance prediction behavior using the genetic and subcategory information of cancer diseases. Subcategorization analysis can decode many useful information of the disease and can help in better treatment modeling. We have shown for the first time how we can use the subcategory information to extract sensitivity of cancer cell lines for different drugs, a procedure never tried before. We have utilized the best practices of the feature training for deep learning to determine the interacting genes for particular drugs.

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Authors and Affiliations

  1. Computer & Information Science & Engineering Department, University of Florida, Gainesville, FL, USA

    Chiranjib Sur

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  1. Chiranjib Sur
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Correspondence to Chiranjib Sur.

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Sur, C. A Multi-Modular System-Genetics (MMSG) Approach for Deep Representation Learning for Personalized Treatment of Cancer Using Sensitivity Analysis of Precision Drugs and Gene Expression Data. Data-Enabled Discov. Appl. 3, 11 (2019). https://doi.org/10.1007/s41688-019-0035-8

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