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from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import cross_val_score rf = RandomForestClassifier(n_estimators=100) scores = cross_val_score(rf, X_train, y_train, cv=5)
Traditional t-tests and ANOVA are the starting point. However, in bioinformatics, these are modified extensively. For example, the (popularized by the limma package in R) uses empirical Bayes methods to shrink variances across genes, providing stable results even with small sample sizes. statistical methods in bioinformatics pdf
: Includes parameter estimation and hypothesis testing, which provide the from sklearn
Bioinformatics relies on several mathematical branches to organize and interpret molecular information: Probability Theory these are modified extensively. For example
In bioinformatics, we often test thousands of genes simultaneously. This creates a statistical trap: the more tests you run, the higher the chance of finding a "significant" result by pure luck (Type I Error).