A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example.

ABSTRACT: Data scarcity is one of the most critical issues impeding the development of prediction models for chemical effects. Multitask learning algorithms leveraging knowledge from relevant tasks showed potential for dealing with tasks with limited data. However, current multitask methods mainly focus on learning from datasets whose task labels are available for most of the training samples. Since datasets were generated for different purposes with distinct chemical spaces, the conventional multitask learning methods may not be suitable. This study presents a novel multitask learning method MTForestNet that can deal with data scarcity problems and learn from tasks with distinct chemical space. The MTForestNet consists of nodes of random forest classifiers organized in the form of a progressive net

SUBMITTER: Lin RH 

PROVIDER: S-EPMC11297603 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature