Download PDF
CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 099-103
DOI: 10.1055/s-0043-1768745
Section 1: Bioinformatics and Translational Informatics
Synopsis

Intersecting Pathways in Bioinformatics and Translational Informatics: A One Health Perspective on Key Contributions and Future Directions

Mary Lauren Benton
1   Department of Computer Science, Baylor University, USA
,
Scott McGrath
2   CITRIS Health, University of California Berkeley, USA
,
Section Editors for the IMIA Yearbook Section on Bioinformatics and Translational Informatics › Author Affiliations
› Further Information
Also available at
   Permissions and Reprints

Summary

Objectives: To identify and summarize the top bioinformatics and translational informatics (BTI) papers published in 2022 for the International Medical Informatics Association (IMIA) Yearbook 2023.

Methods: We conducted a comprehensive literature search to identify the top BTI papers, resulting in a set of ten candidate papers. The candidates were reviewed by the section co-editors and external reviewers to select the top three papers from 2022.

Results: From a total of 558 papers, we identified a final candidate list of ten BTI papers for peer-review. These papers apply new statistical frameworks and experimental designs to better capture individual variability in disease and incorporate data that captures differences between single cells and across environmental exposures. In addition, they highlight the importance of model generalization across diverse cohorts and scalability to large medical centers.

Conclusions: We note several important trends in the candidate top BTI papers this year, including a continued focus on developing accurate and scalable computational models to predict disease risk across diverse cohorts and new strategies to capture the molecular heterogeneity of disease.

Keywords

Bioinformatics - genomics - transcriptomics - machine learning - precision medicine


Publication History

Article published online:
26 December 2023

© 2023. 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. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Trinh P, Zaneveld JR, Safranek S, Rabinowitz PM. One health relationships between human, animal, and environmental microbiomes: a mini-review. One Health Relationships Between Human, Animal, and Environmental Microbiomes: A Mini-Review. Front Public Health 2018 Aug 30;6:235. doi: 10.3389/fpubh.2018.00235.
    PubMed
  • 2 Timme RE, Wolfgang WJ, Balkey M, Gubbala Venkata SL, Randolph R, et al. Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens. One Health Outlook 2020;2(1):20. doi: 10.1186/s42522-020-00026-3.
    PubMed
  • 3 Lustgarten JL, Zehnder A, Shipman W, Gancher E, Webb TL. Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA). JAMIA Open 2020 Apr 11;3(2):306-17. doi: 10.1093/jamiaopen/ooaa005.
    PubMed
  • 4 Grazioli F, Siarheyeu R, Alqassem I, Henschel A, Pileggi G, Meiser A. Microbiome-based disease prediction with multimodal variational information bottlenecks. PLoS Comput Biol 2022 Apr 11;18(4):e1010050. doi: 10.1371/journal.pcbi.1010050.
    PubMed
  • 5 Kuppe C, Ramirez Flores RO, Li Z, Hayat S, Levinson RT, Liao X, et al. Spatial multi-omic map of human myocardial infarction. Nature 2022 Aug;608(7924):766-77. doi: 10.1038/s41586-022-05060-x.
    PubMed
  • 6 Weitz P, Wang Y, Kartasalo K, Egevad L, Lindberg J, Grönberg H, et al. Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks. Bioinformatics 2022 Jun 27;38(13):3462-9. doi: 10.1093/bioinformatics/btac343.
    PubMed
  • 7 Cooper K, Clarke M, Clayton JB. Informatics for your Gut: at the Interface of Nutrition, the Microbiome, and Technology. Yearb Med Inform 2023 Jul 6:89-98. doi: 10.1055/s-0043-1768723.
    PubMed
  • 8 Wang Z, Emmerich A, Pillon NJ, Moore T, Hemerich D, Cornelis MC, et al. Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention. Nat Genet 2022 Sep;54(9):32-1344. doi: 10.1038/s41588-022-01165-1.
    PubMed
  • 9 Wang L, Desai H, Verma SS, Le A, Hausler R, Verma A, et al. Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank. Genet Med 2022 Mar;24(3):601-609. doi: 10.1016/j.gim.2021.10.015.
    PubMed
  • 10 Mishra A, Malik R, Hachiya T, Jürgenson T, Namba S, Posner DC, et al. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature 2022 Nov;611(7934):115-23. doi: 10.1038/s41586-022-05165-3.
    PubMed
  • 11 Wang L, Scherer SE, Bielinski SJ, Muzny DM, Jones LA, Black JL, et al. Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study. Genet Med 2022 May;24(5):1062-72. doi: 10.1016/j.gim.2022.01.022.
    PubMed
  • 12 Robbe P, Ridout KE, Vavoulis DV, Dréau H, Kinnersley B, Denny N, et al. Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features. Nat Genet 2022 Nov;54(11):1675-89. doi: 10.1038/s41588-022-01211-y.
    PubMed
  • 13 Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 2022 May;54(5):581-92. doi: 10.1038/s41588-022-01062-7.
    PubMed
  • 14 Tran QT, Alom MZ, Orr BA. Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors. BMC Bioinformatics 2022 Jun 8;23(1):223. doi: 10.1186/s12859-022-04764-1.
    PubMed