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Categorical data - Astrophysics Data System

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Last Updated: 10 July 2022

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A categorical data clustering framework on graph representation

Clustering categorical data is a common task of machine learning, considering that the type of data is widely available in the real world. A graph-based representation of categorical data is suggested in this context, which finds that the representation of categorical values from their similar graph is used to produce similar charts for similar categorical values. On benchmark data sets, we compared the proposed framework with other display schemes for categorical data clustering.

Source link: https://ui.adsabs.harvard.edu/abs/2022PatRe.12808694B/abstract


A Superstatistical Variational Model for Categorical Data: Applications to Protein Sequence Variation

Understanding the genetic and biophysical factors that control protein structure and function is vital to our understanding of evolutionary and biophysical factors that control protein structure and function. Understanding the constraints on amino acid sequences within a protein family is key to our understanding of evolutionary and biophysical factors that influence protein structure and function. We need interactive models that can reproduce de novo protein sequences in order to get a better picture of what makes an amino acid sequence a functional protein. In addition, probability of point mutations is predictive of fitness results, with reported high probability mutations corresponding to near-neutral fitness costs.

Source link: https://ui.adsabs.harvard.edu/abs/2022APS..MARK06008A/abstract


Modeling rates of disease with missing categorical data

Public health officials and researchers interested in interpreting surveillance results obtained during a public health crisis such as the COVID-19 pandemic can gain valuable insight into aging, sex, and ethnicity. When the geographical distribution of the population covariates is known, the model is locally identifiable, and we show that it is also localizable, and reported cases can be attributed to a spatial unit of observation. Using data from the Michigan Department of Health and Human Services, we can estimate spatial variation in cumulative COVID-19 incidence in Wayne County, Michigan. Non-white residents of Michigan's early part of the COVID-19 pandemic were understated relative risk estimates by race during the early stages of the COVID-19 pandemic in the United States' COVID-19 pandemic in comparison to white people whose race was misrepresented or misrepresented using MI, according to the study.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220608161T/abstract

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions