Home > Uncategorized > Machine learning techniques in the biomedical literature

Machine learning techniques in the biomedical literature

There are relatively few articles published on using machine learning techniques on what many would consider “classical” biomedical study designs (e.g a sample size of 200 and about 10 parameters) and approaches to dealing with . But they may start being published. This is a list to get going with. No all of the article below fit into the above criteria but I’ve kept them here as they’re interesting (at least to me).

This post was motivated by this question on Crossvalidated. I will add to it as I find them or people point them out to me. It’s very short at the moment! Let me know of any broken links.

Articles
Statnikov A, Wang L, Aliferis CF A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics. 2008 Jul 22;9:319.

Van Loon K, Guiza F, Meyfroidt G, Aerts JM, Ramon J, Blockeel H, Bruynooghe M, Van Den Berghe G, Berckmans D. Dynamic data analysis and data mining for prediction of clinical stability. Stud Health Technol Inform. 2009;150:590-4.

Luaces O, Taboada F, Albaiceta GM, DomĂ­nguez LA, EnrĂ­quez P, Bahamonde A; GRECIA Group.Predicting the probability of survival in intensive care unit patients from a small number of variables and training examples.Artif Intell Med. 2009 Jan;45(1):63-76. Epub 2009 Jan 29.

Wu TT, Chen YF, Hastie T, Sobel E, Lange K. Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics. 2009 Mar 15;25(6):714-21. Epub 2009 Jan 28.

Schwaighofer A, Schroeter T, Mika S, Blanchard G. Comb Chem High Throughput Screen. How wrong can we get? A review of machine learning approaches and error bars. 2009 Jun;12(5):453-68.

Huang H, Chanda P, Alonso A, Bader JS, Arking DE. Gene-based tests of association. PLoS Genet. 2011 Jul;7(7):e1002177. Epub 2011 Jul 28.

Liu Z, Shen Y, Ott J. Multilocus association mapping using generalized ridge logistic regression. BMC Bioinformatics. 2011 Sep 29;12:384.

Theses

Hug, Caleb W. Predicting the risk and trajectory of intensive care patients using survival models. 2006 Massachusetts Institute of Technology

Talks / slides / videos:
Victoria Stodden’s slides

  1. December 16, 2011 at 4:07 pm

    Hello my friend,
    I see that I accidentally put your global feed for the blog, and I now moved to use only the R tag feed: http://gossetsstudent.wordpress.com/tag/r/feed for http://www.r-bloggers.com

    Cheers,
    Tal

    • December 16, 2011 at 4:09 pm

      Hi Tal,

      Yes – sorry up to now most of my posts are about R but this one isn’t.

      Andrew

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