Literature DB >> 33523131

mlr3proba: An R Package for Machine Learning in Survival Analysis.

Raphael Sonabend1, Franz J Király1, Andreas Bender2, Bernd Bischl2, Michel Lang2.   

Abstract

MOTIVATION: 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 for survival analysis. This is problematic considering its importance in fields like medicine, bioinformatics, economics, engineering, and more. mlr3proba provides a comprehensive machine learning interface for survival analysis and connects with mlr3's general model tuning and benchmarking facilities to provide a systematic infrastructure for survival modeling and evaluation. AVAILABILITY: mlr3proba is available under an LGPL-3 license on CRAN and at https://github.com/mlr-org/mlr3proba, with further documentation at https://mlr3book.mlr-org.com/survival.html.
© The Author(s) 2021. Published by Oxford University Press.

Entities:  

Year:  2021        PMID: 33523131     DOI: 10.1093/bioinformatics/btab039

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  5 in total

1.  An imputation approach using subdistribution weights for deep survival analysis with competing events.

Authors:  Shekoufeh Gorgi Zadeh; Charlotte Behning; Matthias Schmid
Journal:  Sci Rep       Date:  2022-03-09       Impact factor: 4.379

2.  TTN mutations predict a poor prognosis in patients with thyroid cancer.

Authors:  Xiudan Han; Jianrong Chen; Jiao Wang; Jixiong Xu; Ying Liu
Journal:  Biosci Rep       Date:  2022-07-29       Impact factor: 3.976

3.  Neoadjuvant tislelizumab and tegafur/gimeracil/octeracil (S-1) plus oxaliplatin in patients with locally advanced gastric or gastroesophageal junction cancer: Early results of a phase 2, single-arm trial.

Authors:  Yuping Yin; Yao Lin; Ming Yang; Jianbo Lv; Jiaying Liu; Ke Wu; Ke Liu; Anshu Li; Xiaoming Shuai; Kailin Cai; Zheng Wang; Guobin Wang; Jianfeng Shen; Peng Zhang; Kaixiong Tao
Journal:  Front Oncol       Date:  2022-08-30       Impact factor: 5.738

4.  Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.

Authors:  Raphael Sonabend; Andreas Bender; Sebastian Vollmer
Journal:  Bioinformatics       Date:  2022-07-12       Impact factor: 6.931

5.  A Few-Shot Learning Approach Assists in the Prognosis Prediction of Magnetic Resonance-Guided Focused Ultrasound for the Local Control of Bone Metastatic Lesions.

Authors:  Fang-Chi Hsu; Hsin-Lun Lee; Yin-Ju Chen; Yao-An Shen; Yi-Chieh Tsai; Meng-Huang Wu; Chia-Chun Kuo; Long-Sheng Lu; Shauh-Der Yeh; Wen-Sheng Huang; Chia-Ning Shen; Jeng-Fong Chiou
Journal:  Cancers (Basel)       Date:  2022-01-17       Impact factor: 6.639

  5 in total

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