CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 116-119
DOI: 10.1055/s-0042-1742538
Section 1: Bioinformatics and Translational Informatics

2021 Bioinformatics and Translational Informatics Best Papers

Mary Lauren Benton
1   Assistant Professor, Department of Computer Science, Baylor University, Waco, TX, USA
Scott Patrick McGrath
2   Academic Program Management Officer, CITRIS Health, University of California Berkeley, Missoula, MT, USA
› Institutsangaben


Objectives: To identify and summarize the top bioinformatics and translational informatics papers published in 2021 for the IMIA Yearbook.

Methods: We performed a broad literature search to retrieve Bioinformatics and Translational Informatics (BTI) papers and coupled this with a series of editorial and peer reviews to identity the top papers in the area.

Results: We identified a final candidate list of 15 BTI papers for peer-review; from these candidates, the top three papers were chosen to highlight in this synopsis. These papers expand the integration of multi-omics data with electronic health records and use advanced machine learning approaches to tailor models to individual patients. In addition, our honorable mention paper foreshadows the growing impact of BTI research on precision medicine through the continued development of large clinical consortia.

Conclusion: In the top BTI papers this year, we observed several important trends, including the use of deep-learning approaches to analyse diverse data types, the development of integrative and web-accessible bioinformatics pipelines, and a continued focus on the power of individual genome sequencing for precision health.

Section Editors for the IMIA Yearbook Section on Bioinformatics and Translational Informatics


Artikel online veröffentlicht:
04. Dezember 2022

© 2022. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (

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  • References

  • 1 Chen Q, Allot A, Lu Z. Keep up with the latest coronavirus research. Nature 2020 Mar;579(7798):193.
  • 2 Moses DA, Metzger SL, Liu JR, Anumanchipalli GK, Makin JG, Sun PF, et al. Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria. N Engl J Med 2021 Jul 15;385(3):217-27.
  • 3 Alkenani AH, Li Y, Xu Y, Zhang Q. Predicting Alzheimer’s Disease from Spoken and Written Language Using Fusion-Based Stacked Generalization. J Biomed Inform 2021 Jun;118:103803.
  • 4 Létinier L, Jouganous J, Benkebil M, Bel-Létoile A, Goehrs C, Singier A, et al. Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions. Clin Pharmacol Ther 2021 Aug;110(2):392-400.
  • 5 Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M, Lipkova J, et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 2021 Jun;594(7861):106-10.
  • 6 Veturi Y, Lucas A, Bradford Y, Hui D, Dudek S, Theusch E, et al. A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts. Nat Genet 2021 Jul;53(7):972-81.
  • 7 Eijsbouts C, Zheng T, Kennedy NA, Bonfiglio F, Anderson CA, Moutsianas L, et al. Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders. Nat Genet 2021 Nov;53(11):1543-52.
  • 8 Schwartzentruber J, Cooper S, Liu JZ, Barrio-Hernandez I, Bello E, Kumasaka N, et al. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat Genet 2021 Mar;53(3):392-402.
  • 9 Giannoula A, Centeno E, Mayer MA, Sanz F, Furlong LI. A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities. Bioinformatics 2021 Jun 16;37(10):1435-43.
  • 10 Liu Y, Elsworth B, Erola P, Haberland V, Hemani G, Lyon M, et al. EpiGraphDB: a database and data mining platform for health data science. Bioinformatics 2021 Jun 9;37(9):1304-11.
  • 11 Mountjoy E, Schmidt EM, Carmona M, Schwartzentruber J, Peat G, Miranda A, et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat Genet 2021 Nov;53(11):1527-33.
  • 12 Wei Q, Ramsey SA. Predicting chemotherapy response using a variational autoencoder approach. BMC Bioinformatics 2021 Sep 22;22(1):453.
  • 13 The 100,000 Genomes Project Pilot Investigators. 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care — Preliminary Report. N Engl J Med 2021 Nov 11;385(20):1868–80.
  • 14 Ramoni RB, Mulvihill JJ, Adams DR, Allard P, Ashley EA, Bernstein JA, et al. The Undiagnosed Diseases Network: Accelerating Discovery about Health and Disease. Am J Hum Genet 2017 Feb 2;100(2):185-92.
  • 15 Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 2019 Apr;51(4):584-91.
  • 16 Wand H, Lambert SA, Tamburro C, Iacocca MA, O‘Sullivan JW, Sillari C, et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature 2021 Mar;591(7849):211-9.