Identification of functionally relevant genetic variants associated with 
multiple sclerosis using deep learning

Arloth, J.; Eraslan, G.; Andlauer, T.; Gieger, C.; Gold, R.; Heilmann-Heimbach, S.; Kacprowski, T.; Meitinger, T.; Laudes, M.; Luessi, F.; Mueller-Myhsok, B.; Nischwitz, S.; Peters, A.; Paul, F.; Rawal, R.; Strauch, K.; Wiendl, H.; Hemmer, B.; Theis, F.; Binder, E.; Mueller, N.

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Meeting Abstract

Identification of functionally relevant genetic variants associated with multiple sclerosis using deep learning

MPS-Authors

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Arloth, J.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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Andlauer, T.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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Mueller-Myhsok, B.
RG Statistical Genetics, Max Planck Institute of Psychiatry, Max Planck Society;

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Nischwitz, S.
Max Planck Institute of Psychiatry, Max Planck Society;

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Binder, E.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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Citation

Arloth, J., Eraslan, G., Andlauer, T., Gieger, C., Gold, R., Heilmann-Heimbach, S., et al. (2019). Identification of functionally relevant genetic variants associated with multiple sclerosis using deep learning. MULTIPLE SCLEROSIS JOURNAL, 25, 906-907.

Cite as: https://hdl.handle.net/21.11116/0000-0009-6F10-9

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