Classification of bond types by clustering analysis on phase diagrams In Fig. 2e–g, we have explored the model parameter space to identify regions that correspond to slip-only bonds and catch-slip bonds. Here we examined whether, and if so, how parameters that best-fit different experimental ...
Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Disease Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression Proxy Endpoints - Bridging clinical trials and ...
Clustering: It is a method of organizing the data in a group of multiple classes where the objects... Learn more about this topic: Data Mining: Applications & Examples from Chapter 3/ Lesson 4 11K Data mining is the process of extracting and analyzing data from a variety of sources, typic...
Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance Robin N. Beaumont, Gareth Hawkes, Adam C. Gunning & Caroline F. Wright Genome Medicine volume 16, Article number: 64 (2024) Cite this article 1727 Accesses 16 Altmetric Me...
The clustering just above the 30% threshold suggests that some firms find it costly to pay a high dividend percentage. That these firms appear to prefer paying just enough to meet the regulation is consistent with their attempting to avoid the regulatory scrutiny faced by explaining firms. In ...
Category clusteringThe measurement of individual differences in cognitive processes and the advancement of multinomial processing tree (MPT) models were two of William H. Batchelder's major research interests. Inspired by his work, we investigated developmental differences between 7-year-old children, 10...
(ML) tree is shown in Fig.1a. The unsupervised K-means clustering performed on patristic distances, coreSNPs or Mash distances converged in dividing theS. marcescenspopulation in five well-distinguished clusters (Fig.S1). The clusters are coherent with the phylogenetic clades (Figs.1aandS2) and...
Transfers of resources in dictator games vary significantly by the characteristics of recipients. We focus on social norms and demonstrate that variation i
We build risk classes according to each region’s risk of exposure to COVID-19 cases by performing a 1-dimensional k-means38 unsupervised clustering algorithm on the number of cases for each wave, with a varying number of clusters: we found that two clusters is an optimal choice, in terms...