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Table 1 Summary, strength, and limitation of each method of machine learning and systems genomics (MLSG) software frameworks

From: Machine learning and systems genomics approaches for multi-omics data

Software framework Summary Strength Limitation
Model-based integration (MBI) Multiple predictive models are generated by using various multi-omics data types; then a final predictive model is generated by using the multiple models. Predictive models can be consolidated from various multi-omics data types, and each data type can be gathered from a various set of patients with same phenotype. It may be challenging to avoid overfitting.
Concatenation-based integration (CBI) Multiple data matrices of different multi-omics data types are incorporated into a large input matrix; then a predictive model is generated by using the large input matrix. It is fairly easy to leverage various machine learning methods for analyzing continuous or categorical data once a large input matrix is formed. It may be challenging to combine a large input matrix.
Transformation-based integration (TBI) Datasets for various multi-omics data types are first converted into intermediate forms, which are united into a large input matrix; then a predictive model is generated by using the large input matrix. Unique variables such as patient identifiers can be used to link multi-omics data types and integrate a variety of continuous or categorical data values. It may be challenging to transform into intermediate forms.