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Generative Adversarial Networks in Precision Oncology

Published: 26 September 2019 Publication History

Abstract

Precision medicine strives to deliver improved care based on genetic patient information. Towards this end, it is crucial to find effective data representations on which to perform matching and inference operations. We develop and evaluate a generative adversarial neural network (GAN) approach to representation learning with the goal of patient-centric literature retrieval and treatment recommendation in precision oncology. Several large-scale corpora including the COSMIC Cancer Gene Census, COSMIC Mutation Data, Genomic Data Commons (GDC) and 26M MEDLINE abstracts are used to train GANs for synthesizing genetic mutation patterns that likely correspond to patient properties such as their demographics or cancer type. The introduction of GANs into the literature retrieval and treatment recommendation process results in significant improvements in performance by increasing the recall of a range of methods at stable precision. Finally, we propose a method to discover novel gene-gene interaction hypotheses to guide future research.

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  • (2023)Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology EnterpriseJournal of Clinical Medicine10.3390/jcm1214483012:14(4830)Online publication date: 21-Jul-2023

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cover image ACM Conferences
ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval
September 2019
273 pages
ISBN:9781450368810
DOI:10.1145/3341981
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 September 2019

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Author Tags

  1. deep learning
  2. gan
  3. oncology
  4. precision medicine

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  • Short-paper

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  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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ICTIR '19
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ICTIR '19 Paper Acceptance Rate 20 of 41 submissions, 49%;
Overall Acceptance Rate 235 of 527 submissions, 45%

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View all
  • (2023)Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology EnterpriseJournal of Clinical Medicine10.3390/jcm1214483012:14(4830)Online publication date: 21-Jul-2023

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