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. …”

Table 5_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 7_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. …”

Flowchart data balancing algorithm (SMOTE). by Xiangting Shi (21439579)

Computational cost of anchor-based algorithms. by Olarewaju Mubashiru Lawal (14710251)

Mortality of MM patients based on the MYLORD algorithm (2014–2020). by Cyrille Touzeau (14982494)

Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes by Vijay Subramanian (20718933)

Automated Analysis of Astrocyte Cell Connectivity After Laser Ablation Using Machine Learning and Path-Finding Algorithms by Connor Lee (22327243)

“…Astrocytes, critical glial cells in the central nervous system, exhibit calcium spikes in response to injury, but manual annotation of imaging data is time-consuming and prone to bias. The study integrates Cellpose 2.0 for automated segmentation of nuclei and cytoplasm with a heuristic A* path-finding algorithm to classify cellular connectivity. …”

External cohort raw data. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”

Internal cohort raw data. by Lei Gao (6566)

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models.…”

Data Sheet 1_MetaboLINK is a novel algorithm for unveiling cell-specific metabolic pathways in longitudinal datasets.csv by Jared Lichtarge (20548571)

“…For the first time, we applied the PCA-GLASSO algorithm (i.e., MetaboLINK) to metabolomics data derived from Nuclear Magnetic Resonance (NMR) spectroscopy performed on neural cells at various developmental stages, from human embryonic stem cells to neurons.…”

FDR and Runtime for ALARM. by Nand Sharma (21519845)

Subjects:

FDR (). by Nand Sharma (21519845)

Subjects: