Slice Algorithm

SLICE An algorithm for incorporating ultrasonography in the assessment of shocked or breathless patientsHuge gratitude to Lorena Zhang CCPU, Justin Bowra, Minh-Tu Duong amp the Royal North Shore Hospital Team in Sydney, NSW for sharing their research.SLICE is an algorithm for the integration of point-of-care ultrasound in the assessment and resuscitation of the shocked or breathless patient. It

SLICE is an algorithm that utilizes single-cell RNA-seq scRNA-seq data to quantitatively measure cellular differentiation states based on single cell entropy and predict cell differentiation lineages via the construction of entropy directed cell trajectories. Developed by Minzhe Guo.

algorithm. He computed slices by solving the data flow problem of relevant nodes. He gave algorithms for intraprocedural and interprocedural slicing. However, the interprocedural version did not account for the calling context and therefore produced imprecise slices. Ottenstein et al. OtO84, FeOW87 recognized that intraprocedural backward

The SLICE Single Cell Lineage Inference Using Cell Expression Similarity and Entropy algorithm consists of two major steps 1 measuring cell differentiation states based on the calculation of single cell entropy scEntropy and 2 predicting cell differentiation trajectories by ordering single cells according to their scEntropy-derived differentiation states.

slice sampling to adaptively choose the magnitude of changes made. It is therefore attractive for routine and automated use. Slice sampling methods Metropolis algorithm can be used to sample from many of the complex, multivariate distributions encountered in statistics. However, to implement Gibbs

We developed SLICE, a novel algorithm that utilizes single-cell RNA-seq scRNA-seq to quantitatively measure cellular differentiation states based on single cell entropy and predict cell

Slice sampling Neal, 2003 is a Markov Chain Monte Carlo MCMC method for sampling from a probability distribution. The basic idea behind a slice sample is that any distribution can be sampled from by selecting uniformly spaced points under a probability distribution curve using a MCMC algorithm multivariate distributions can be sampled by

This paper, therefore, develops the Slice algorithm which can be accurately trained on low-dimensional, dense deep learning features popularly used to represent queries and which efficiently scales to 100 million labels and 240 million training points. Slice achieves this by reducing the training and prediction times from linear to logarithmic

The design of the SLICE algorithm. A The schematic flow of the SLICE algorithm.B The schematic flow of scEntropy.To validate the accuracy and robustness of SLICE predictions, we applied SLICE to three independent scRNA-seq data sets 7,13,14 with known lineage and developmental time information.Results showed that scEntropy decreased with the progression of cellular differentiation stages

Slice sampling is a type of Markov chain Monte Carlo algorithm for pseudo-random number sampling, i.e. for drawing random samples from a statistical distribution.The method is based on the observation that to sample a random variable one can sample uniformly from the region under the graph of its density function. 1 2 3