An Attention-Aware Multi-Task Learning Framework Identifies Candidate Targets for Drug Repurposing in Sarcopenia.

Md Selim Reza, Chuan Qiu, Xu Lin, Kuan-Jui Su, Anqi Liu, Xiao Zhang, Yun Gong, Zhe Luo, Qing Tian, Martin Nwadiugwu, Shaung Liang, Hui Shen, Hong-Wen Deng

Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana, USA. · pubmed

Paper

Abstract

Sarcopenia presents a pressing public health concern due to its association with age-related muscle mass decline, strength loss and reduced physical performance, particularly in the growing older population. Given the absence of approved pharmacological therapies for sarcopenia, the need to discover effective pharmacological interventions has become critical.

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Research summary

The paper proposes a multi-task learning framework to identify potential drug targets for repurposing in the treatment of sarcopenia. This research is relevant as it addresses a significant age-related condition and seeks to find pharmacological interventions, which could contribute to improving healthspan in the aging population.

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