Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.

ABSTRACT:

Introduction

Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sensitive to certain immunotherapeutic agents is of great clinical relevance.

Methods

We propose an ELISE (Ensemble Learning for Immunotherapeutic Response Evaluation) pipeline to generate a robust and highly accurate approach to predicting individual responses to immunotherapies. ELISE employed iterative univariable logistic regression to select genetic features of patients, using Monte Carlo Tree Search (MCTS) to tune hyperparameters. In each trial, ELISE selected multiple models for integration based on add or concatenate stacking

SUBMITTER: Jin W 

PROVIDER: S-EPMC9751999 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature