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Predictions, Pivots, and a Pandemic: a Review of 2020's Top Translational Bioinformatics Publications.
McGrath, Scott P; Benton, Mary Lauren; Tavakoli, Maryam; Tatonetti, Nicholas P.
  • McGrath SP; CITRIS Health, University of California Berkeley, Berkeley, CA, USA.
  • Benton ML; Department of Computer Science, Baylor University, Waco, TX, USA.
  • Tavakoli M; MTERMS Lab, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Tatonetti NP; Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Yearb Med Inform ; 30(1): 219-225, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1392953
ABSTRACT

OBJECTIVES:

Provide an overview of the emerging themes and notable papers which were published in 2020 in the field of Bioinformatics and Translational Informatics (BTI) for the International Medical Informatics Association Yearbook.

METHODS:

A team of 16 individuals scanned the literature from the past year. Using a scoring rubric, papers were evaluated on their novelty, importance, and objective quality. 1,224 Medical Subject Headings (MeSH) terms extracted from these papers were used to identify themes and research focuses. The authors then used the scoring results to select notable papers and trends presented in this manuscript.

RESULTS:

The search phase identified 263 potential papers and central themes of coronavirus disease 2019 (COVID-19), machine learning, and bioinformatics were examined in greater detail.

CONCLUSIONS:

When addressing a once in a centruy pandemic, scientists worldwide answered the call, with informaticians playing a critical role. Productivity and innovations reached new heights in both TBI and science, but significant research gaps remain.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Computational Biology / Machine Learning / COVID-19 Type of study: Experimental Studies / Prognostic study Language: English Journal: Yearb Med Inform Year: 2021 Document Type: Article Affiliation country: S-0041-1726540

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computational Biology / Machine Learning / COVID-19 Type of study: Experimental Studies / Prognostic study Language: English Journal: Yearb Med Inform Year: 2021 Document Type: Article Affiliation country: S-0041-1726540