This is a list of my academic work. For talks and similar, visit the talks page.

I defended my Ph. D. in February, 2019. My dissertation is called Small data: practical modeling issues in human-model -omic data.

Published

• Theodor A. Ross, Jessin Janice, Sergio Arredondo-Alonso, Iren H. Löhr, Einar Holsbø, Jukka Corander, Anna K. Pöntinen, Michael Kampffmeyer, and Kristin Hegstad. Enterococcus lactis is ecologically and genetically distinct from the major opportunistic pathogen enterococcus faecium. Microbial Genomics, 11(6)2025.
• M. Askar, L. Småbrekke, E. Holsbø, L. A. Bongo, and K. Svendsen. Using network analysis modularity to group health code systems and decrease dimensionality in machine learning models. Exploratory Research in Clinical and Social Pharmacy, 14:100463, 2024.
• B.-R. Pedersen, R. K. Johansen, E. Holsbø, H. L. Sommerseth, and L. A. Bongo. More efficient manual review of automatically transcribed tabular data. Historical Life Course Studies, 14:3-15, 2024/06/30 2024.
• Pedersen BR, Holsbø E, Andersen T, Shvetsov N, Ravn J, Sommerseth HL, Bongo LA. Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes. (data) Historical Life Course Studies 2022; Volum 11. ISSN 2352-6343.s 1 - 17.s
• Holsbø E, Olsen KS. Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort. Frontiers in oncology. 2020 Oct 15;10:2277.
• Holsbø E., Perduca V., Bongo L. A., Lund E., & Birmelé E. (2020). Predicting breast cancer metastasis from whole-blood transcriptomic measurements. BMC Research Notes, 13(1), 1-5.
• T. E. Skar, E. Holsbø, K. Svendsen, and L. A. Bongo. Interactive exploration of population scale pharmacoepidemiology datasets. In Proceedings of the 11th acm international conference on bioinformatics, computational biology and health informatics, BCB ’20, New York, NY, USA, 2020. , Association for Computing Machinery.
• Dong C, Metzger M, Holsbø E, Perduca V, Carbonnel F. Systematic review with meta‐analysis: mortality in acute severe ulcerative colitis. Aliment Pharmacol Ther. 2019; 00: 1– 27.
• Jácome C., Ravn J., Holsbø E., Aviles-Solis J. C., Melbye, H. & Bongo, L. A. Convolutional Neural Network for Breathing Phase Detection in Lung Sounds. Sensors 19, (2019). [arXiv]
• Holsbø E., & Perduca V. (2018). Shrinkage estimation of rate statistics. Case Studies in Business, Industry & Government Statistics, 7(1). [arXiv]
• Tartari G, Tiede L, Holsbø E, Knudsen K, Raknes IA, Fjukstad B, Mode N, Bjørndalen JM, Lund E, and Bongo LA. Mr. Clean: A Tool for Tracking and Comparing the Lineage of Scientific Visualization Code. 2nd IEEE Working Conference on Software Visualization
• Elahi N, Karlsen R, and Holsbø E. Personalized Photo Recommendation By Leveraging User Modeling On Social Network. Proceedings of the iiWAS2013 The 15th International Conference on Information Integration and Web-based Applications & Services.
• Holsbø E, Ha PH, and Anshus O. The Big Digger & Puzzler System for Harvesting & Analyzing Data from Social Networks. Norsk informatikkonferanse NIK 2013

Book chapter

• Holsbø E, Møllersen K. Woes of The Practicing Omics Researcher. Universitetsforlaget 2020 ISBN 978-82-15-04119-3.s 77 - 94.
• Fjukstad B, Shvetsov N, Nøst TH, Bøvelstad HM, Halbach T, Holsbø E, Hansen, K, Bongo, LA. Reproducible Data Management and Analysis using R. Universitetsforlaget 2020 ISBN 978-82-15-04119-3.s 48 - 62.s

Preprint

• J. Bangard, E. Holsbø, K. Svendsen, V. Perduca, and E. Birmelé. Detecting adverse high-order drug interactions from individual case safety reports using computational statistics on disproportionality measures. 2025. [arXiv]
• K. Møllersen and E. Holsbø. Accounting for multiplicity in machine learning benchmark performance. 2024. [arXiv]
• E. Holsbø, V. Perduca, L. A. Bongo, E. Lund, and E. Birmelé, Stratified time-course gene preselection shows a pre-diagnostic transcriptomic signal for metastasis in blood cells: a proof of concept from the NOWAC study [bioRxiv], 2018.
• M. Grønnesby, J. C. A. Solis, E. Holsbø, H. Melbye, and L. A. Bongo, Machine Learning Based Crackle Detection in Lung Sounds, [arXiv], 2017.
• H. M. Bøvelstad, E. Holsbø, L. A. Bongo, and E. Lund, A Standard Operating Procedure For Outlier Removal In Large-Sample Epidemiological Transcriptomics Datasets, 2017. [bioRxiv].

Other

• Dong C, Metzger M, Holsbø E, Perduca V, Carbonnel Fr. Letter: how can we reduce mortality in elderly patients with acute severe ulcerative colitis? Authors’ reply. Alimentary Pharmacology and Therapeutics 2020; Volum 51 (12). ISSN 0269-2813.s 1445 - 1446.s
• Olsen KS, Holsbø E, Rognmo K, Krum-Hansen S, and Lund E. Stress related to a suspicious mammogram - potential transcriptomic effects. Abstract. The 7th Conference on Epidemiology and Registry-Based Health Research - NordicEpi 2015

As advisor

PhD students

• Mohsen Askar, Predicting Norwegian elderly hospitalizations using Machine Learning. Doctoral thesis, 2025. (co-advisor)

Master’s students

• Zin Anwar, Insights into the Biofilm-associated Genetic Background of Escherichia coli. Master thesis, 2025. (co-advisor)
• Elias E.G. Riise, Improving automated underwater ship hull inspection through incremental learning & uncertainty quantification in deep learning models. Master thesis, 2023.
• Erling Devold, Through Space and Time. Master thesis, 2023
• Tengel Skar, Scalable exploration of population-scale drug consumption data. Master thesis, 2019. (co-advisor)
• Mike Voets, Deep Learning: From Data Extraction to Large-Scale Analysis. Master thesis, 2018. (co-advisor)
• Morten Grønnesby, Automated Lung Sound Analysis. Master thesis, 2016. (co-advisor)