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  • stanford's machine learning applied in bioinformatics

    2 questions:

    1) Anyone on this forum also taking the ml-class offered?
    2) Specific examples of machine learning used in bioinformatics

    So I'm about half way through the class and I started with the specific intent of applying this to bioinformatics ...

    And while I understand what I'm working on ... I'd really like more practice!

    linear regression
    logistic regression
    neural networks
    support vector machines

  • #2
    Shameless self promotion. Paper describing application of SVMs for the prediction of putative vaccine candidates from bacterial genome sequence.

    Paul

    Comment


    • #3
      I'm ok with shameless!

      sounds .... amazingly spot on... !

      if( published? )
      " where can I find one? "
      else
      " WHEN!? "
      end

      and I've got a class full of smart and optimistic kids looking to learn some things ... do you have any leads .. or like annoying pet projects you'd like to talk about?

      Comment


      • #4
        Oops,
        forgot to include link!

        Reverse vaccinology aims to accelerate subunit vaccine design by rapidly predicting which proteins in a pathogenic bacterial proteome are putative protective antigens. Support vector machine classification is a machine learning approach that has been applied to solve numerous classification problems …

        Comment


        • #5
          Hi,

          I am following the ml class. The techniques taught in the class are very useful in biology.

          Actually until I took the class, I didnot realize that the techniques used in microarray data analysis (or RNA-seq), for example, cluster analysis, PCA, clustering, are machine learning techniques.

          Plus, linear regression is used a lot in modeling gene expression and gene set analysis, e.g. limma, GSEAlm.

          Moreover, Octave, the programming environment used in the course (or a free version of Matlab), is also widely used in bioinformatics.

          You won't regret taking the course.

          Cheers,
          Jun

          Comment


          • #6
            Paul:


            ^ Im posting this guy to reddit

            Jun:
            Might I ask how you came upon that fact? Where can I read more about it?

            Comment


            • #7
              Originally posted by delinquentme View Post
              Paul:


              ^ Im posting this guy to reddit

              Jun:
              Might I ask how you came upon that fact? Where can I read more about it?
              Just a few examples as mentioned in my post.

              Multivariate analysis package for microarray (clustering, PCA, COA ...)
              Culhane AC, Thioulouse J, Perriere G, Higgins DG (2005) MADE4: an R package for multivariate analysis of gene expression data. Bioinformatics 21:2789-2790.

              Linear model in microarray DE gene
              Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3.

              Linear model in gene set analysis
              Oron AP, Jiang Z, Gentleman R (2008) Gene set enrichment analysis using linear models and diagnostics. Bioinformatics 24:2586-2591.

              Comment

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