CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 190-193
DOI: 10.1055/s-0039-1677945
Section 8: Bioinformatics and Translational Informatics
Georg Thieme Verlag KG Stuttgart

Contributions from the 2018 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 Paris Descartes, 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:
16 August 2019 (online)



Objectives: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) 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 two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers.

Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer.

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.