Literature DB >> 34953466

Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering.

Bo Zhang1, Jianghua He1, Jinxiang Hu1, Devin C Koestler1, Prabhakar Chalise1.   

Abstract

Understanding the relationship between molecular markers and a phenotype of interest is often obfuscated by patient-level heterogeneity. To address this challenge, Chang et al. recently published a novel method called Component-wise Sparse Mixture Regression (CSMR), a regression-based clustering method that promises to detect heterogeneous relationships between molecular markers and a phenotype of interest under high-dimensional settings. In this Letter to the Editor, we raise awareness to several issues concerning the assessment of CSMR in Chang et al., particularly its assessment in settings where the number of features, P, exceeds the study sample size, N, and advocate for additional metrics/approaches when assessing the performance of regression-based clustering methodologies.
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Entities:  

Keywords:  disease heterogeneity; mixture modeling; supervised learning

Mesh:

Year:  2022        PMID: 34953466      PMCID: PMC8769908          DOI: 10.1093/bib/bbab532

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   13.994


  7 in total

1.  Stability-based validation of clustering solutions.

Authors:  Tilman Lange; Volker Roth; Mikio L Braun; Joachim M Buhmann
Journal:  Neural Comput       Date:  2004-06       Impact factor: 2.026

2.  A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis.

Authors:  G W Milligan; M C Cooper
Journal:  Multivariate Behav Res       Date:  1986-10-01       Impact factor: 5.923

3.  Supervised clustering of high-dimensional data using regularized mixture modeling.

Authors:  Wennan Chang; Changlin Wan; Yong Zang; Chi Zhang; Sha Cao
Journal:  Brief Bioinform       Date:  2021-07-20       Impact factor: 11.622

4.  Finding reproducible cluster partitions for the k-means algorithm.

Authors:  Paulo J G Lisboa; Terence A Etchells; Ian H Jarman; Simon J Chambers
Journal:  BMC Bioinformatics       Date:  2013-01-14       Impact factor: 3.169

5.  The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

Authors:  Jordi Barretina; Giordano Caponigro; Nicolas Stransky; Kavitha Venkatesan; Adam A Margolin; Sungjoon Kim; Christopher J Wilson; Joseph Lehár; Gregory V Kryukov; Dmitriy Sonkin; Anupama Reddy; Manway Liu; Lauren Murray; Michael F Berger; John E Monahan; Paula Morais; Jodi Meltzer; Adam Korejwa; Judit Jané-Valbuena; Felipa A Mapa; Joseph Thibault; Eva Bric-Furlong; Pichai Raman; Aaron Shipway; Ingo H Engels; Jill Cheng; Guoying K Yu; Jianjun Yu; Peter Aspesi; Melanie de Silva; Kalpana Jagtap; Michael D Jones; Li Wang; Charles Hatton; Emanuele Palescandolo; Supriya Gupta; Scott Mahan; Carrie Sougnez; Robert C Onofrio; Ted Liefeld; Laura MacConaill; Wendy Winckler; Michael Reich; Nanxin Li; Jill P Mesirov; Stacey B Gabriel; Gad Getz; Kristin Ardlie; Vivien Chan; Vic E Myer; Barbara L Weber; Jeff Porter; Markus Warmuth; Peter Finan; Jennifer L Harris; Matthew Meyerson; Todd R Golub; Michael P Morrissey; William R Sellers; Robert Schlegel; Levi A Garraway
Journal:  Nature       Date:  2012-03-28       Impact factor: 49.962

6.  Stability-based validation of dietary patterns obtained by cluster analysis.

Authors:  Nicolas Sauvageot; Anna Schritz; Sonia Leite; Ala'a Alkerwi; Saverio Stranges; Faiez Zannad; Sylvie Streel; Axelle Hoge; Anne-Françoise Donneau; Adelin Albert; Michèle Guillaume
Journal:  Nutr J       Date:  2017-01-14       Impact factor: 3.271

7.  Drug sensitivity prediction with high-dimensional mixture regression.

Authors:  Qianyun Li; Runmin Shi; Faming Liang
Journal:  PLoS One       Date:  2019-02-27       Impact factor: 3.240

  7 in total

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