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Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs
View ORCID ProfileNicole Ludwig, View ORCID ProfileTobias Fehlmann, Manfred Gogol, Walter Maetzler, Stephanie Deutscher, Simone Gurlit, Claudia Schulte, Anna-Katharina von Thaler, Christian Deuschle, Florian Metzger, Daniela Berg, Ulrike Suenkel, View ORCID ProfileVerena Keller, View ORCID ProfileChristina Backes, View ORCID ProfileHans-Peter Lenhof, View ORCID ProfileEckart Meese, Andreas Keller
doi: https://doi.org/10.1101/638213
Nicole Ludwig
1Department of Human Genetics, Saarland University, Homburg, Germany
Tobias Fehlmann
2Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
Manfred Gogol
3Institut für Gerontologie, Universität Heidelberg, Heidelberg, Germany
Walter Maetzler
4Department of Neurology, Christian-Albrechts-University of Kiel, Kiel, Germany
5Center for Neurology and Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen, Tuebingen, Germany and DZNE, German Center for Neurodegenerative Diseases, Tuebingen, Germany
Stephanie Deutscher
1Department of Human Genetics, Saarland University, Homburg, Germany
Simone Gurlit
6Department of Anesthesiology and Intensive Care, St. Franziskus Hospital Muenster, Muenster, Germany
Claudia Schulte
5Center for Neurology and Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen, Tuebingen, Germany and DZNE, German Center for Neurodegenerative Diseases, Tuebingen, Germany
Anna-Katharina von Thaler
5Center for Neurology and Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen, Tuebingen, Germany and DZNE, German Center for Neurodegenerative Diseases, Tuebingen, Germany
Christian Deuschle
5Center for Neurology and Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen, Tuebingen, Germany and DZNE, German Center for Neurodegenerative Diseases, Tuebingen, Germany
Florian Metzger
7Department of Psychiatry and Psychotherapy, University Hospital Tuebingen, Tuebingen, Germany
Daniela Berg
4Department of Neurology, Christian-Albrechts-University of Kiel, Kiel, Germany
5Center for Neurology and Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen, Tuebingen, Germany and DZNE, German Center for Neurodegenerative Diseases, Tuebingen, Germany
Ulrike Suenkel
5Center for Neurology and Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, University of Tuebingen, Tuebingen, Germany and DZNE, German Center for Neurodegenerative Diseases, Tuebingen, Germany
Verena Keller
8Department of Medicine II, Saarland University Medical Center, Homburg/Saar, Germany
Christina Backes
2Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
Hans-Peter Lenhof
9Center for Bioinformatics, Saarland Informatics Campus, Saarbrücken, Germany
Eckart Meese
1Department of Human Genetics, Saarland University, Homburg, Germany
Andreas Keller
2Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
9Center for Bioinformatics, Saarland Informatics Campus, Saarbrücken, Germany
Article usage
Posted May 14, 2019.
Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs
Nicole Ludwig, Tobias Fehlmann, Manfred Gogol, Walter Maetzler, Stephanie Deutscher, Simone Gurlit, Claudia Schulte, Anna-Katharina von Thaler, Christian Deuschle, Florian Metzger, Daniela Berg, Ulrike Suenkel, Verena Keller, Christina Backes, Hans-Peter Lenhof, Eckart Meese, Andreas Keller
bioRxiv 638213; doi: https://doi.org/10.1101/638213
Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs
Nicole Ludwig, Tobias Fehlmann, Manfred Gogol, Walter Maetzler, Stephanie Deutscher, Simone Gurlit, Claudia Schulte, Anna-Katharina von Thaler, Christian Deuschle, Florian Metzger, Daniela Berg, Ulrike Suenkel, Verena Keller, Christina Backes, Hans-Peter Lenhof, Eckart Meese, Andreas Keller
bioRxiv 638213; doi: https://doi.org/10.1101/638213
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