Center for Health Data Science publications¶

Updated: 2023-03-28

HeaDS believes in the principles of open science and reproducibility. As part of our commitment to these values, we strive to make our publications as reproducible as possible. To achieve this, we make our research process transparent by showcasing the GitHub repositories that contain our code and data. We also document our research process to ensure that others can replicate our findings and build upon them. By doing so, we hope to contribute to the wider scientific community's efforts to promote openness and transparency in research.

Krogh group¶

The deep generative decoder: Using MAP estimates of representations¶

DGD is a package for probabilistic representation learning. It can be applied to various tasks. The implementation for Fashion-MNIST and single-cell expression counts can be found in branch paper.

Karaderi group¶

Yengo, L., Vedantam, S., Marouli, E. et al. A saturated map of common genetic variants associated with human height. Nature 610, 704-71 (2022). https://doi.org/10.1038/s41586-022-05275-y

Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, EU-STANDS4PM consortium, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine-Guidelines and Recommendations for Data Integration and Model Validation. Journal of Personalized Medicine. 2022; 12(2):166. https://doi.org/10.3390/jpm12020166

Barin Burc, Yoldascan Banu Elcin, Savaskan Fatma, Ozbalikci Goncagul, Karaderi Tugce, Çakal Hüseyin. Joint Investigation of 2-Month Post-diagnosis IgG Antibody Levels and Psychological Measures for Assessing Longer Term Multi-Faceted Recovery Among COVID-19 Cases in Northern Cyprus. Front. Public Health. Volume 8 - 2021. https://doi.org/10.3389/fpubh.2020.590096