Performance metrics (compared with DXA-based measurement) for different prediction algorithms using all predictors in test data, based on 25 datasets where the DXA-based outcomes were randomly permuted (to provide a null or baseline scenario to compare against performance on the real data). by Nick Birk (15444728)

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UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Mean Absolute Error (compared with DXA-based measurement) for different prediction algorithms using all predictors in test data, and additional performance metrics for best model (n = 289 female, 318 male). by Nick Birk (15444728)

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RE-RRT* algorithm. by Naeem Ul Islam (19837966)

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Comparison of city decoders trained on session one and session two data. by Sam Hall-McMaster (10343795)

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The original data used in this study for analysis. by Xin Ma (37268)

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Results of each filtering method on the point cloud in the example region(a: September 2023 single epoch point cloud; b: the example region; c: manual filtering results; d: Filteri... by Xiaowei Li (620087)

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Poster: Norma: A Framework for Finding Threshold Associations Between Continuous Variables Using Point-wise Function by Md Mahin (21434966)

“…To be able to compute such hotspots, the paper proposes a novel grid-based spatial hotspot-growing algorithm which computes regions of a point-wise function above a given threshold. …”

Data for Figs 3–9. by Xiaogang Li (299205)

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Image 2_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 6_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 2_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 4_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Image 3_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 8_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 3_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Image 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.tiff by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”