Results 21 to 30 of about 25,673,599 (348)
Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis [PDF]
International Journal of Ophthalmology, 2020 AIM: To identify metastatic genes and miRNAs and to investigate the metastatic mechanism of uveal melanoma (UVM). METHODS: GSE27831, GSE39717, and GSE73652 gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database, and the
Dan-Dan Zhao, Xin Zhao, Wen-Tao Li
doaj +1 more source
Glioma Survival Analysis Empowered With Data Engineering—A Survey
IEEE Access, 2021 Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity. In clinical practice, clinicians estimate the survival with their experience, which can be biased and optimistic.
Navodini Wijethilake +5 more
semanticscholar +1 more source
Multiscale Bayesian survival analysis [PDF]
The Annals of Statistics, 2021 We consider Bayesian nonparametric inference in the right-censoring survival model, where modeling is made at the level of the hazard rate. We derive posterior limiting distributions for linear functionals of the hazard, and then for `many' functionals simultaneously in appropriate multiscale spaces.
Castillo, Ismaël +1 more
openaire +4 more sources
A survival analysis of COVID-19 in the Mexican population
BMC Public Health, 2020 At present, the Americas report the largest number of cases of COVID-19 worldwide. In this region, Mexico is the third country with most deaths (20,781 total deaths).
G. Salinas-Escudero +5 more
semanticscholar +1 more source
lifelines: survival analysis in Python
Journal of Open Source Software, 2019 One frustration of data scientists and statisticians is moving between programming languages to complete projects. The most common two are R and Python. For example, a survival analysis model may be fit using R’s survival-package (Terry M Therneau, 2015)
Cameron Davidson-Pilon
semanticscholar +1 more source
Survival Analysis: A Self-Learning Text
, 1997 Introduction to Survival Analysis.- Kaplan-Meier Survival Curves and the Log-Rank Test.- The Cox Proportional Hazards Model and Its Characteristics.- Evaluating the Proportional Hazards Assumption.- The Stratified Cox Procedure.- Extension of the Cox ...
D. Kleinbaum
semanticscholar +1 more source
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
AAAI Conference on Artificial Intelligence, 2018 Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) survival times ...
Changhee Lee +3 more
semanticscholar +1 more source
Medical Technology and Public Health Journal, 2022 Corona Virus Disease2019 (COVID-19) is a disease that shocked the world at the end of 2019. Based on data, positive cases of Covid-19 in Indonesia on July 29, 2021 reached 3,331,206 people, with 3,240,654 Covid-19 patients recovering and 90,552 Covid-19
Iqbal Firdaus Iqbal +3 more
doaj +1 more source
mlr3proba: an R package for machine learning in survival analysis
Bioinform., 2020 Summary As machine learning has become increasingly popular over the last few decades, so too has the number of machine-learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support
R. Sonabend +4 more
semanticscholar +1 more source
Deep Recurrent Survival Analysis [PDF]
AAAI Conference on Artificial Intelligence, 2018 Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship ...
Kan Ren +6 more
semanticscholar +1 more source