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Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants - Supplementary Information
journal contribution
posted on 2017-11-25, 09:34 authored by Giorgio ValentiniGiorgio Valentini, Max Schubach, Matteo Re, Peter RobinsonThis is the Supplementary information of the paper:
M. Schubach, M. Re, P.N. Robinson and G. Valentini Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants,
Scientific Reports, Nature Publishing, 7:2959, 2017
M. Schubach, M. Re, P.N. Robinson and G. Valentini Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants,
Scientific Reports, Nature Publishing, 7:2959, 2017
Funding
This work was supported by grants from the National Institute of Health (NIH) Monarch Initiative (NIH OD #5R24OD011883), the Bundesministerium für Bildung und Forschung (BMBF project number 01EC1402B) and by the DAAD Funding program Research Stays for University Academics and Scientists (ID 57210259).
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machine Learning Classificationmachine learning for imbalanced dataprediction of deleterious genetic variantsSingle Nucleotide VariantsPathogenic variants screeningGenetic Mendelian diseasesGenetic analysis resultsBioinformatics SoftwarePattern Recognition and Data MiningHealth InformaticsApplied Computer Science
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