ROC curves and AUC scores to compare the classifier performance of the different data sets containing various number of features. by Aimilia-Christina Vagiona (17584758)

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Components of a critical PPI network involved in SCA1 pathogenesis. by Aimilia-Christina Vagiona (17584758)

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Edges of the hPIN. by Aimilia-Christina Vagiona (17584758)

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Evaluation of the model. by Aimilia-Christina Vagiona (17584758)

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Properties of the 14 features that were used for building the model. by Aimilia-Christina Vagiona (17584758)

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Structure of the work presented in this manuscript. by Aimilia-Christina Vagiona (17584758)

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Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

<b>Figures for Protein-Protein interaction (PPI) network of differentially acetylated proteins</b><b> </b><b>by Aspirin during differentiation of THP-1 cell towards macrophage</b> by Zi-Hui Ma (14118552)

“…The PPI network was further analyzed to identify densely interconnected functional clusters/modules using topological clustering algorithms. …”

Data Sheet 1_Accurate informatic modeling of tooth enamel pellicle interactions by training substitution matrices with Mat4Pep.doc by Jeremy Horst Keeper (20458274)

Unsupervised Machine Learning for Differential Analysis in Proteomics by Guanyang Xu (22379531)

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Left: Quality profile of LAFM images for 1) periplasmic (top) and 2) cytoplasmic (bottom) SecYEG. by Creighton M. Lisowski (22756444)

Computational Pipeline for Accelerating the Design of Glycomimetics by Yao Xiao (227567)

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Screening of candidate genes via protein–protein interaction (PPI) network analysis and subsequent correlation analysis. A. A PPI network was constructed to investigate the interac... by Xiaoyu Shen (4715430)

Low plDDT scores correlate with poor structural quality. by Samuel Eriksson Lidbrink (21616146)

Fits of selected conformations to experimental SANS data. by Samuel Eriksson Lidbrink (21616146)

Fit of the change in SANS intensity. by Samuel Eriksson Lidbrink (21616146)

Clustering quality across the reruns of the pipeline. by Samuel Eriksson Lidbrink (21616146)

Cluster separation for different AF-quality metrics. by Samuel Eriksson Lidbrink (21616146)

Change in experimental SANS profiles corresponds to the difference between generated states. by Samuel Eriksson Lidbrink (21616146)

Root mean square fluctuation (RMSF) of the AF-generated conformations and the MD-generated ensembles. by Samuel Eriksson Lidbrink (21616146)