Titlebook: Machine Learning and Deep Learning in Computational Toxicology; Huixiao Hong Book 2023 This is a U.S. government work and not under copyri

非實體

elucidate

1分開

Emerging Machine Learning Techniques in Predicting Adverse Drug Reactionsemerging machine learning models, including deep learning and graph-based models, as potential solutions to address this challenge were reviewed. As more data become available, it will become more feasible to make use of the complex data and emerging technologies to develop more accurate models to identify ADRs and protect patients from ADRs.

終端

未完成

你正派

缺乏

Graph Kernel Learning for Predictive Toxicity Modelsons, challenges, and perspectives about the GKL techniques for toxicity-related problems. We hope this chapter could help better understand and guide applications of GKL in solving computational toxicity problems.

ligature

Optimize and Strengthen Machine Learning Models Based on in Vitro Assays with Mechanistic Knowledge injury as an example. Another challenge for developing predictive models using in vitro assay data is the difficulty to corroborate the result with human data due to the scarcity of suitable datasets. We partially address this problem by taking advantage of real-world data. A novel statistical meth

Tartar

ablate