Abstract: Systems and methods for subject clinical condition evaluation using a plurality of modules are provided. Modules comprise features whose corresponding feature values associate with an absence, presence or stage of phenotypes associated with the clinical condition. A first dataset is obtained having feature values, acquired through a first technical background from respective subjects in transcriptomic, proteomic, or metabolomic form, for at least a first of the plurality of modules. A second training dataset is obtained having feature values, acquired through a technical background other than the first technical background, from training subjects of the second dataset, in the same form as for the first dataset, of at least the first module. Inter-dataset batch effects are removed by co-normalizing feature values across the training datasets, thereby calculating co-normalized feature values used to train a classifier for clinical condition evaluation of the test subject.

Abstract: Systems and methods for subject clinical condition evaluation using a plurality of modules are provided. Modules comprise features whose corresponding feature values associate with an absence, presence or stage of phenotypes associated with the clinical condition. A first dataset is obtained having feature values, acquired through a first technical background from respective subjects in transcriptomic, proteomic, or metabolomic form, for at least a first of the plurality of modules. A second training dataset is obtained having feature values, acquired through a technical background other than the first technical background, from training subjects of the second dataset, in the same form as for the first dataset, of at least the first module. Inter-dataset batch effects are removed by co-normalizing feature values across the training datasets, thereby calculating co-normalized feature values used to train a classifier for clinical condition evaluation of the test subject.