Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the negative impact caused by false negative labels, we study how these labels affect the model's explanation. We observe that the explanation of two models, trained with full and partial labels each, highlights similar regions but with different scaling, where the latter tends to have lower attribution scores. Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels. Even with the conceptually simple approach, the multi-label classification performance improves by a large margin in three different datasets on a single positive label setting and one on a large-scale partial label setting. Code is available at https://github.com/youngwk/BridgeGapExplanationPAMC.

In clinical proteomics, a major goal is to identify important proteins in proteomics data that is based on biological samples. Using biological connections such as protein interactions that link directly or indirectly proteins from earlier studies with those in current proteomics studies, we propose a new algorithm called ranking with interaction chains (RIC) to rank protein biomarker candidates. With RIC, we use statistical measures to set the best parameter values and applied it to proteomics-based breast cancer biomarker studies. Such ranking strategy may also be generalized where a functional priority score for each biomolecule can be computed from different Omics data types.