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Applying glmnet to identify predictors for subtypes

Final answer. Keep in mind I work in Python so I'm trying to translate here. The output of coef(cv.lassoModel) ... those are your genes of interest thats your ...
M__'s user avatar
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1 vote

Are there applications of semi-supervised learning to genomics?

Personal view Semi-supervised learning is basically unsupervised learning with weights, so the "label" or "category" can be quantified rather than approximately clustered as occurs ...
M__'s user avatar
  • 12.6k
1 vote

Intepreting and applying ordinal logistic regression coefficients to calculate probabilities?

Its a while since I've done GLM because ML is the method of choice. Question1 Yes multi-linear regression is an approximate description of GLM. Generally the weights simply mean whether a feature is ...
M__'s user avatar
  • 12.6k
1 vote

Is a classification tree appropriate method to use for my project?

This isn't a cool strategy because dendrograms are very poor representations of the relationships between genomes, for example they use UPGMA style metrics fail to account for mutation rate variation ...
M__'s user avatar
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1 vote

Advise on building an effect ML model for predicting important proteins for drug response

The missing values might be an issue indeed. You might want to use imputation methods, e.g. ...
rkellerm's user avatar
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