Until now, this approach, which allows to explain some classic clustering criteria such as the well-known k -means criteria and to propose general criteria, has been developed to classify a set of objects measured on a set of variab 基群分析在混合物模型成为了一种古典和强有力的方法。 直到...
TreeSHAP is an algorithm to compute SHAP values for tree ensemble models such as decision trees, random forests, and gradient boosted trees in a polynomial-time proposed by Lundberg et. al (2018)¹. The algorithm allows us to reduce the complexity from O(TL2^M)to O(TLD^2) (T = numb...
That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Waiting for the next birthday to come seems “forever” for children, but the older we get, the faster “time flies”. Yet, it is an open question why that is. One possible answer relates to...
For arguing between agents, we used the Jason multi-agent platform. We assume different knowledge base and reasoning capabilities for each agent. The agents have their own optical coherence tomography (OCT) images on which they apply a distinct machine learning algorithm. The learned model is used...
The algorithm used by BAYPASS is well suited to study systems involving nested or hierarchical population structure (Gautier 2015), which is particularly common in dendritic habitats such as freshwater (Thomaz et al. 2016). We tested for GEA associations accounting for assumed population demographic ...
three-component waveforms (up to 4 Hz) for two co-located random double-couple mechanisms based on a regional velocity model for Mayotte27. The synthetics are calculated using the pyrocko toolbox and a precalculated Green’s function database91obtained via the orthonormal propagator algorithm QSE...
4C). Hierarchical clustering evaluation of differentially expressed miRNAs indicated that the control and PS NPs samples were clustered together (Fig. 4D). The relevant information is shown in Table S3. A total of 7 miRNAs were not homologous with humans, including miR-667–3p, miR-3084–3p, ...
However, many works in the literature use the network average clustering coefficient to analyze network properties. The network average clustering coefficient weights more nodes with a low degree (as discussed in the Supplementary Information Sect. 2). Thus, it is not a correct measure to analyze ...
Clustering analysis on empirical and simulated neuronal responses To evaluate the optimal number of clusters that can best describe both the empirical weight distributions as well as the simulated neuronal responses, Dirichlet process with Gaussian mixture modelling58and time-series K-Means analysis81were...
the AMPure XP system (Beckman Coulter, Beverly, USA). The quality and quantity of the cDNA library were determined using an Agilent 2100 bioanalyzer (Agilent, Santa Clara, CA, USA). Clustering of the index-coded samples was performed on a cBot Cluster Generation System using the TruSeq PE ...