Multimodal Self-Paced Learning with a Soft Weighting Scheme for Robust Classification of Multiomics Data

摘要

A robust multimodal data integration method, termed the SMSPL technique, aimed at simultaneously predicting subtypes of cancers and identifying potentially significant multiomics signatures, is provided. The SMSPL technique leverages linkages among different types of data to interactively recommend high-confidence training samples during classifier training. Particularly, a new soft weighting scheme is adopted to assign weights to training samples of each type, thus more faithfully reflecting latent importance of samples in self-paced learning. The SMSPL technique iterates between calculating the sample weights from training loss values and minimizing weighted training losses for classifier updating, allowing the classifiers to be efficiently trained. In classifying a test sample, outputs of the trained classifiers are integrated to yield a class label by solving an optimization problem for minimizing a sum of classifier losses in selecting a candidate class label, making the SMSPL technique more accruable to discriminate equivocal samples.