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_posts/2024-02-09-MOGDxPPMI.md

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@@ -15,7 +15,7 @@ MOGDx is a flexible tool to integrate multiple omic measures and perform classif
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In this paper, we look at two disease subgroups: those who have a mutation in a casaul gene for PD, labelled *Genetic* and those who have no known genetic cause or sporadic onset, labelled *Idiopathic*. Using MOGDx, we have tested all available combinations of genomic data from the PPMI dataset. We highlight the performance of the best performing modalities in the figure on the right by comparing it to the worst performing modality and a baseline clinical assessment modality called the MDS-UPDRS. We obtain strongest performance when classifying in the subgroup who have a mutation in a casaul gene. We found that no single modality or combination of modality achieved optimal performance at every time point in the idiopathic subgroup, highlighting the importance of flexible modality integration. We also found that worst performance is achieved when the two subgroups are considered jointly. DNAm was predicitve for all experiments at almost every timepoint, indicating the presence of an epigenetic modification between individuals with PD and those without, regardless of subgroup.
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<img style="margin-left: 1rem" align="left" src="assets/year3_flow.png" width = "1000px" >
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<img style="margin-left: 1rem" align="left" src="/assets/year3_flow.png" width = "1000px" >
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Finally, we found that a combination of SNP and DNAm achieved excellent stratification accuracy in the genetic subgroup at all time points. Optimal performance was observed by a model trained at year 3, the latest time point available in the PPMI dataset. Our results show that this combination of modalities could be used as an early diagnostic tool and such a tool should be trained using PD patients who have progressed to a later disease stage.
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