A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction.

ABSTRACT:

Motivation

Predicting the response of cell lines to characteristic drugs based on multi-omics gene information has become the core problem of precision oncology. At present, drug response prediction using multi-omics gene data faces the following three main challenges: first, how to design a gene probe feature extraction model with biological interpretation and high performance; second, how to develop multi-omics weighting modules for reasonably fusing genetic data of different lengths and noise conditions; third, how to construct deep learning models that can handle small sample sizes while minimizing the risk of possible overfitting.

Results

We propose an innovative drug response prediction model (NMDP). First, the NMDP model introduces an interpretable semi-supervised wei

SUBMITTER: Miao R 

PROVIDER: S-EPMC11968432 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature