A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI).

Journal: Computers in biology and medicine
PMID: 35240378

Abstract

BACKGROUND: Although copy number variations (CNVs) are infrequent, each anomaly is unique, and multiple CNVs can appear simultaneously. Growing evidence suggests that CNVs contribute to a wide range of diseases. When CNVs are detected, assessment of their clinical significance requires a thorough literature review. This process can be extremely time-consuming and may delay disease diagnosis. Therefore, we have developed CNV Extraction, Transformation, and Loading Artificial Intelligence (CNV-ETLAI), an innovative tool that allows experts to classify and interpret CNVs accurately and efficiently.

Authors

  • Jongmun Choi
    Department of Radiology, Laboratory of Medical Imaging and Computation, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA; Department of Laboratory Medicine, Hanyang University College of Medicine, Seoul, South Korea; GC Genome, GC Laboratories, Yong-in, South Korea.
  • Soomin Jeon
    Department of Radiology, Laboratory of Medical Imaging and Computation, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA.
  • Doyun Kim
    Department of Emergency Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Republic of Korea.
  • Michelle Chua
    Department of Radiology, Laboratory of Medical Imaging and Computation, Massachusetts General Brigham and Harvard Medical School, Boston, MA, USA.
  • Synho Do
    Department of Radiology, Massachusetts General Hospital, Boston, MA, USA. sdo@mgh.harvard.edu.

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