CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 188-192
DOI: 10.1055/s-0040-1702002
Section 8: Bioinformatics and Translational Informatics
Georg Thieme Verlag KG Stuttgart

Contributions from the 2019 Literature on Bioinformatics and Translational Informatics

Malika Smaïl-Tabbone
1  Loria UMR 7503, Université de Lorraine, CNRS, Inria Nancy Grand-Est, Nancy, France
Bastien Rance
2  HEGP, AP-HP & Université de Paris, UMRS 1138 Centre de Recherche des Cordeliers, INSERM, Paris, France
Section Editors for the IMIA Yearbook Section on Bioinformatics and Translational Informatics› Author Affiliations
Further Information

Publication History

Publication Date:
21 August 2020 (online)


Objectives: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook.

Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers.

Results: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tuberculosis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer’s Disease.

Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.