Results 41 to 50 of about 631,153 (290)
Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods
Scientific ReportsCOVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g., temperature ...
Lihao Xiao +6 more
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An Analysis of Implied Volatility, Sensitivity, and Calibration of the Kennedy Model
MathematicsThe Kennedy model provides a flexible and mathematically consistent framework for modeling the term structure of interest rates, leveraging Gaussian random fields to capture the dynamics of forward rates.
Dalma Tóth-Lakits +2 more
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Tail probability approximations for Student's t-statistics [PDF]
, 2006 10.1007/s00440-005-0494-8Probability Theory and Related Fields1364541 ...
Zhou, Wang +3 more
core +1 more source
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
On Drift Parameter Estimation in Models with Fractional Brownian Motion by Discrete Observations
Austrian Journal of Statistics, 2014 We study a problem of an unknown drift parameter estimation in a stochastic differen- tial equation driven by fractional Brownian motion. We represent the likelihood ratio as a function of the observable process.
Yuliya Mishura, Kostiantyn Ralchenko
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Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models
Machine Learning and Knowledge Extraction, 2020 An artificial neural network (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features. This machine performs well in many application areas such as classification and prediction from
Kaixu Yang, Tapabrata Maiti
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A Comparison of Likelihood and Bayesian Inference for the Threshold Parameter in the Inverse Gaussian Distribution [PDF]
, 1998 Communications in Statistics - Theory and Methods2792173 ...
Yang, Z. +3 more
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COMP–PMEPA1 axis promotes epithelial‐to‐mesenchymal transition in breast cancer cells
Molecular Oncology, EarlyView.This study reveals that cartilage oligomeric matrix protein (COMP) promotes epithelial‐to‐mesenchymal transition (EMT) in breast cancer. We identify PMEPA1 (protein TMEPAI) as a novel COMP‐binding partner that mediates EMT via binding to the TSP domains of COMP, establishing the COMP–PMEPA1 axis as a key EMT driver in breast cancer.
Konstantinos S. Papadakos +6 more
wiley +1 more source
Nature CommunicationsIn spatial transcriptomics, tissue samples are randomly positioned. Rotation-sensitive methods can lead to unreliable spatially variable gene (SVG) detection.
Haohao Su, Yuehua Cui
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Canadian Journal of Gastroenterology, 1996 Standard therapeutic approaches to acid peptic disorders have dealt with neutralizing or inhibiting aggressive factors and/or bolstering defensive factors.
WR Yacoub +3 more
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