.png){kind=icon}
返回
Designing interpretable deep learning applications for functional genomics: a quantitative analysis
.png){kind=icon}
OA
AI
DOI：10.1093/bib/bbae449.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex interactions between and within omics data; however, interpreting and explaining these models can be challenging. Interpretability is essential not only to help progress our understanding of the biological mechanisms underlying traits and diseases but also for establishing trust in these model's efficacy for healthcare applications. Recognizing this importance, recent years have seen the development of numerous diverse interpretability strategies, making it increasingly difficult to navigate the field. In this review, we present a quantitative analysis of the challenges arising when designing interpretable deep learning solutions in functional genomics. We explore design choices related to the characteristics of genomics data, the neural network architectures applied, and strategies for interpretation. By quantifying the current state of the field with a predefined set of criteria, we find the most frequent solutions, highlight exceptional examples, and identify unexplored opportunities for developing interpretable deep learning models in genomics.
Keyword:
interpretable
explainable
visible
deep learning
omics
genomics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.7
论文数:
5.6K
被引数:
2.7W
机构
引用论文
分享收藏A pan-tissue DNA-methylation epigenetic clock based on deep learning基于深度学习的泛组织DNA甲基化表观遗传时钟.png)
NPJ AGINGOAAI
NPJ AGINGIF6
OAAI分享收藏
查看更多内容