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How to select and output the informative genes in the resulting model based on SVM?

Hello everyone, I am learning SVM, according to http://compdiag.molgen.mpg.de/ngfn/docs/2005/sep/exercises-classif.pdf Since I am completely a novice, when I follow the book's instructions to learn to encounter the following questions: 1. How to select and output the informative genes in the resulting model? 2.How many genes do you need to still get a reasonable CV error? 3. How to use the model to do ROC curve analysis? Very much looking forward to your reply and script thank you very much!

svm
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written by a511512345 a511512345

Dear Kevin Blighe, Thanks for your help and reminders. In fact, every time before I want to reply your help, but I do not know what is the reason, not every successful reply to you. Maybe for me in China, and not well linked to biostars. I feel so guilty I can not let you know in time that you have helped me to learn a lot, I hope you will continue to help me.

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written by a511512345 a511512345

No problem. Have you read the entire manual?

They perform the following:

  1. select the top 1000 genes based on high variance
  2. apply a support vector machine with linear kernel (for more, read here )
  3. cross-validate the SVM 10 times (for info on cross validation see A: Multinomial elastic net implementation on microarray dataset , error at lognet a )
  4. choose the best predictors based on low error after cross validation
  5. further refine the predictors using a simple t-test
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written by Kevin Blighe Kevin Blighe

Sincerely thank you You always help me in detail About SVM, I have read the manual thoroughly. It is frustrating that I still do not fully understand, do not know which of the important genes to choose. Probably due to my extremely poor programming ability as a doctor. Thank you again!

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written by a511512345 a511512345

The tutorial uses multiple indicators in order to choose important genes, but the most relevant to the tutorial is a low error margin after cross-validation of the SVM linear kernel.

The cross-validation repeats the SVM multiple times (10 times), and then compares the results each time. This allows for the production of error margins. If a gene has very similar values in each cross validation, it's error will be low; if a gene has very different values in each cross-validation, it's error will be high.

谢谢

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written by Kevin Blighe Kevin Blighe

ok, I bite again to study hard! Hope to learn! thank you In addition, your Chinese is very good, ha ha ha ha Thank you

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written by a511512345 a511512345

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