A Data ExploratioN pIpeliNE
ADENINE is a machine learning and data mining Python pipeline that helps you to answer this tedious question: are my data relevant with the problem I’m dealing with?
The main structure of adenine can be summarized in the following 4 steps.
Imputing: Does your dataset have missing entries? In the first step you can fill the missing values choosing between different strategies: feature-wise median, mean and most frequent value or a more stable k-NN imputing.
Preprocessing: Have you ever wondered what would have changed if only your data have been preprocessed in a different way? Or is data preprocessing is a good idea at all? ADENINE offers several preprocessing procedures, such as: data centering, Min-Max scaling, standardization or normalization and allows you to compare the results of the analysis made with different preprocessing step as starting point.
Dimensionality Reduction: In the context of data exploration, this phase becomes particularly helpful for high dimensional data (e.g. -omics scenario). This step includes some manifold learning (such as isomap, multidimensional scaling, etc) and unsupervised dimensionality reduction (principal component analysis, kernel PCA) techniques.
Clustering: This step aims at grouping data into clusters in an unsupervised manner. Several techniques such as k-means, spectral or hierarchical clustering are offered.
The final output of adenine is a compact and textual representation of the results obtained from the pipelines made with each possible combination of the algorithms implemented at each step.
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