Semi-supervised data-integrated feature importance enhances performance and interpretability of biological classification tasks.

ABSTRACT:

Motivation

Accurate model performance on training data does not ensure alignment between the model's feature weighting patterns and human knowledge, which can limit the model's relevance and applicability. We propose Semi-Supervised Data-Integrated Feature Importance (DIFI), a method that numerically integrates a priori knowledge, represented as a sparse knowledge map, into the model's feature weighting. By incorporating the similarity between the knowledge map and the feature map into a loss function, DIFI causes the model's feature weighting to correlate with the knowledge.

Results

We show that DIFI can improve the performance of neural networks using two biological tasks. In the first task, cancer type prediction from gene expression profiles was guided by identities of c

SUBMITTER: Kim JW 

PROVIDER: S-EPMC12261460 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature