Publications

Preprints

    Journal Publications

    • Gossmann, A., Zille, P., Calhoun, V., & Wang, Y.-P. (2018). FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics. IEEE Transactions on Medical Imaging, 37(8), 1761–1774. Retrieved from http://dx.doi.org/10.1109/TMI.2018.2815583 arXiv:1705.04312 [pdf], DOI: 10.1109/TMI.2018.2815583.
    • Gossmann, A., Cao, S., Brzyski, D., Zhao, L. J., Deng, H. W., & Wang, Y. P. (2018). A sparse regression method for group-wise feature selection with false discovery rate control. IEEE/ACM Transactions on Computational Biology and Bioinformatics / IEEE, ACM, 15(4), 1066–1078. Retrieved from http://dx.doi.org/10.1109/TCBB.2017.2780106 DOI: 10.1109/TCBB.2017.2780106.
    • Brzyski, D., Gossmann, A., Su, W., & Bogdan, M. (2018). Group SLOPE – Adaptive Selection of Groups of Predictors. Journal of the American Statistical Association, 1–15. Taylor & Francis. Retrieved from https://doi.org/10.1080/01621459.2017.1411269 arXiv:1610.04960 [pdf], DOI: 10.1080/01621459.2017.1411269.
    • Cao, S., Qin, H., Gossmann, A., Deng, H.-W., & Wang, Y.-P. (2016). Unified tests for fine scale mapping and identifying sparse high-dimensional sequence associations. Bioinformatics, 32(3), 330–337. Retrieved from http://dx.doi.org/10.1093/bioinformatics/btv586 DOI: 10.1093/bioinformatics/btv586.
    • Sammarco, M. C., Simkin, J., Cammack, A. J., Fassler, D., Gossmann, A., Marrero, L., Lacey, M., et al. (2015). Hyperbaric Oxygen Promotes Proximal Bone Regeneration and Organized Collagen Composition during Digit Regeneration. PloS one, 10(10). Public Library of Science. DOI: 10.1371/journal.pone.0140156.

    Conference Papers

    • Gossmann, A., Pezeshk, A., & Sahiner, B. (2018). Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed ’test’ dataset and a potential solution. In Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment. International Society for Optics and Photonics. Retrieved from https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10577/105770K/Test-data-reuse-for-evaluation-of-adaptive-machine-learning-algorithms/10.1117/12.2293818.short?SSO=1 DOI: 10.1117/12.2293818.
    • Cao, S., Qin, H., Gossmann, A., Deng, H.-W., & Wang, Y.-P. (2015). Unified Tests for Fine Scale Mapping and Identifying Sparse High-dimensional Sequence Associations. In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’15 (pp. 241–249). New York, NY, USA: ACM. DOI: 10.1145/2808719.2808744.
    • Gossmann, A., Cao, S., & Wang, Y.-P. (2015). Identification of Significant Genetic Variants via SLOPE, and Its Extension to Group SLOPE. In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’15 (pp. 232–240). New York, NY, USA: ACM. DOI: 10.1145/2808719.2808743.

    Other

    • Gossmann, A. (2012, October). On disjunction and numerical existence properties of extensions of Heyting arithmetic (Bachelor Thesis). Technische Universität Darmstadt. Download PDF.