DynProfiler: a Python package for comprehensive analysis and interpretation of signaling dynamics leveraged by deep learning techniques.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-10-07 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae145
Masato Tsutsui, Mariko Okada
{"title":"DynProfiler: a Python package for comprehensive analysis and interpretation of signaling dynamics leveraged by deep learning techniques.","authors":"Masato Tsutsui, Mariko Okada","doi":"10.1093/bioadv/vbae145","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>Signaling dynamics encode important features and regulatory mechanisms of biological systems, and recent studies have reported the use of simulated signaling dynamics with mechanistic modeling as biomarkers for human diseases. Given the success of deep learning techniques, it is expected that they can extract informative patterns from simulation results more effectively than traditional approaches involving manual feature selection, which can be used for subsequent analyses, such as patient stratification and survival prediction. Here, we propose DynProfiler, which utilizes the entire signaling dynamics, including intermediate variables, as input and leverages deep learning techniques to extract informative features without requiring any labels. Furthermore, DynProfiler incorporates a modern explainable AI solution to provide quantitative time-dependent importance scores for each dynamics. Using simulated dynamics of patients with breast cancer as an example, we demonstrate DynProfiler's ability to extract high-quality features that can predict mortality risk and identify important dynamics, highlighting upregulated phosphorylated GSK3β as a biomarker for poor prognosis. Overall, this tool can be useful for clinical application, as well as for elucidating biological system dynamics.</p><p><strong>Availability and implementation: </strong>The DynProfiler Python library is available in GitHub at https://github.com/okadalabipr/DynProfiler.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae145"},"PeriodicalIF":2.4000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464416/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae145","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Summary: Signaling dynamics encode important features and regulatory mechanisms of biological systems, and recent studies have reported the use of simulated signaling dynamics with mechanistic modeling as biomarkers for human diseases. Given the success of deep learning techniques, it is expected that they can extract informative patterns from simulation results more effectively than traditional approaches involving manual feature selection, which can be used for subsequent analyses, such as patient stratification and survival prediction. Here, we propose DynProfiler, which utilizes the entire signaling dynamics, including intermediate variables, as input and leverages deep learning techniques to extract informative features without requiring any labels. Furthermore, DynProfiler incorporates a modern explainable AI solution to provide quantitative time-dependent importance scores for each dynamics. Using simulated dynamics of patients with breast cancer as an example, we demonstrate DynProfiler's ability to extract high-quality features that can predict mortality risk and identify important dynamics, highlighting upregulated phosphorylated GSK3β as a biomarker for poor prognosis. Overall, this tool can be useful for clinical application, as well as for elucidating biological system dynamics.

Availability and implementation: The DynProfiler Python library is available in GitHub at https://github.com/okadalabipr/DynProfiler.

DynProfiler:利用深度学习技术对信号动态进行综合分析和解释的 Python 软件包。
摘要:信号动力学编码了生物系统的重要特征和调控机制,最近的研究报道了利用模拟信号动力学机理模型作为人类疾病的生物标志物。鉴于深度学习技术的成功,与传统的人工特征选择方法相比,深度学习技术有望更有效地从模拟结果中提取信息模式,并将其用于患者分层和生存预测等后续分析。在此,我们提出了 DynProfiler,它利用包括中间变量在内的整个信号动态作为输入,并利用深度学习技术提取信息特征,而无需任何标签。此外,DynProfiler 还采用了现代可解释人工智能解决方案,为每个动力学提供量化的随时间变化的重要性评分。以乳腺癌患者的模拟动态为例,我们展示了 DynProfiler 提取高质量特征的能力,这些特征可以预测死亡风险并识别重要动态,突出显示上调的磷酸化 GSK3β 是不良预后的生物标志物。总之,该工具可用于临床应用以及阐明生物系统动力学:DynProfiler Python 库可在 GitHub 上获取:https://github.com/okadalabipr/DynProfiler。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
1.60
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信