A method combining LDA and neural networks for antitumor drug efficacy prediction.

ABSTRACT:

Background

Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task.

Objective

This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data.

Methods

This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network

SUBMITTER: Zhu W 

PROVIDER: S-EPMC11384538 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature