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Contributions from the 2019 Literature on Bioinformatics and Translational Informatics
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.
- 1 Butte AJ. Translational bioinformatics: coming of age. J Am Med Inform Assoc 2008; Dec; 15 (06) 709-14
- 2 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (02) 135-44
- 3 Adam TJ, Chi CL. Big Data Cohort Extraction for Personalized Statin Treatment and Machine Learning. In: Larson RS, Oprea TI. Bioinformatics and Drug Discovery [Internet]. New York, NY: Springer New York; 2019 [cited 2020 May 25]. p. 255–72. (Methods Mol Biol 2019;1939.255-72). Available from: http://link.springer.com/10.1007/978-1-4939-9089-4_14
- 4 Barroso-Sousa R, Guo H, Srivastava P, James T, Birch W, Siu LL. et al. Utilization of tumor genomics in clinical practice: an international survey among ASCO members. Future Oncol 2019; Jul; 15 (21) 2463-70
- 5 Béal J, Montagud A, Traynard P, Barillot E, Calzone L. Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients. Front Physiol 2019; 9: 1965
- 6 Chen ML, Doddi A, Royer J, Freschi L, Schito M, Ezewudo M. et al. Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction. EBioMedicine 2019; 43: 356-69
- 7 Chung RH, Kang CY. A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification. GigaScience [Internet]. 2019 May 1 [cited 2020 May 25];8(5):giz045. Available from: https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giz045/5480572
- 8 Esteban-Medina M, Peña-Chilet M, Loucera C, Dopazo J. Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models. BMC Bioinformatics 2019; 20 (01) 370
- 9 Fernández-Navarro P, López-Nieva P, Piñeiro-Yañez E, Carreño-Tarragona G, Martinez-López J, Sánchez Pérez R. , et al. The use of PanDrugs to prioritize anticancer drug treatments in a case of T-ALL based on individual genomic data. BMC Cancer 2019; 19 (01) 1005
- 10 Graim K, Friedl V, Houlahan KE, Stuart JM. PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction. Pac Symp Biocomput 2019; 24: 136-47
- 11 Ibrahim NE, McCarthy CP, Shrestha S, Gaggin HK, Mukai R, Magaret CA. et al. A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography. Clin Cardiol 2019; Feb; 42 (02) 292-8
- 12 Kim IE, Sarkar IN. Racial Representation Disparity of Population-Level Genomic Sequencing Efforts. Stud Health Technol Inform 2019; 264: 974-8
- 13 Kim K, Baik H, Jang CS, Roh JK, Eskin E, Han B. Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes. Genome Biol 2019; 20 (01) 175
- 14 Liu P, Li H, Li S, Leung KS. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinformatics. 2019; 20 (01) 408
- 15 Marttinen M, Paananen J, Neme A, Mitra V, Takalo M, Natunen T. et al. A multiomic approach to characterize the temporal sequence in Alzheimer’s disease-related pathology. Neurobiol Dis 2019; 124: 454-68
- 16 Ruan J, Jahid MDJ, Gu F, Lei C, Huang YW, Hsu YT. , et al. A novel algorithm for network-based prediction of cancer recurrence. Genomics 2019; Jan; 111 (01) 17-23
- 17 Wan N, Weinberg D, Liu TY, Niehaus K, Ariazi EA, Delubac D. et al. Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA. BMC Cancer 2019; 19 (01) 832