PENN: Phase Estimation Neural Network on Gene Expression Data.

Aram Ansary Ogholbake, Qiang Cheng
{"title":"PENN: Phase Estimation Neural Network on Gene Expression Data.","authors":"Aram Ansary Ogholbake, Qiang Cheng","doi":"10.1007/978-3-031-42317-8_5","DOIUrl":null,"url":null,"abstract":"<p><p>With the continuous expansion of available transcriptomic data like gene expression, deep learning techniques are becoming more and more valuable in analyzing and interpreting them. The National Center for Biotechnology Information Gene Expression Omnibus (GEO) encompasses approximately 5 million gene expression datasets from animal and human subjects. Unfortunately, the majority of them do not have a recorded timestamps, hindering the exploration of the behavior and patterns of circadian genes. Therefore, predicting the phases of these unordered gene expression measurements can help understand the behavior of the circadian genes, thus providing valuable insights into the physiology, behaviors, and diseases of humans and animals. In this paper, we propose a novel approach to predict the phases of the un-timed samples based on a deep neural network architecture. It incorporates the potential periodic oscillation information of the cyclic genes into the objective function to regulate the phase estimation. To validate our method, we use mouse heart, mouse liver and temporal cortex of human brain dataset. Through our experiments, we demonstrate the effectiveness of our proposed method in predicting phases and uncovering rhythmic pattern in circadian genes.</p>","PeriodicalId":94283,"journal":{"name":"The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4th : 2023 : Marrakech, Morocco ; Online)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10540272/pdf/nihms-1931232.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4th : 2023 : Marrakech, Morocco ; Online)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-031-42317-8_5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/31 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

With the continuous expansion of available transcriptomic data like gene expression, deep learning techniques are becoming more and more valuable in analyzing and interpreting them. The National Center for Biotechnology Information Gene Expression Omnibus (GEO) encompasses approximately 5 million gene expression datasets from animal and human subjects. Unfortunately, the majority of them do not have a recorded timestamps, hindering the exploration of the behavior and patterns of circadian genes. Therefore, predicting the phases of these unordered gene expression measurements can help understand the behavior of the circadian genes, thus providing valuable insights into the physiology, behaviors, and diseases of humans and animals. In this paper, we propose a novel approach to predict the phases of the un-timed samples based on a deep neural network architecture. It incorporates the potential periodic oscillation information of the cyclic genes into the objective function to regulate the phase estimation. To validate our method, we use mouse heart, mouse liver and temporal cortex of human brain dataset. Through our experiments, we demonstrate the effectiveness of our proposed method in predicting phases and uncovering rhythmic pattern in circadian genes.

PENN:基因表达数据的相位估计神经网络。
随着可用转录组数据(如基因表达)的不断扩展,深度学习技术在分析和解释这些数据方面变得越来越有价值。国家生物技术信息中心基因表达综合数据库(GEO)包含大约500万个来自动物和人类受试者的基因表达数据集。不幸的是,它们中的大多数没有记录的时间戳,阻碍了对昼夜节律基因的行为和模式的探索。因此,预测这些无序基因表达测量的阶段可以帮助理解昼夜节律基因的行为,从而为人类和动物的生理、行为和疾病提供有价值的见解。在本文中,我们提出了一种基于深度神经网络架构的预测非定时样本相位的新方法。它将循环基因的潜在周期振荡信息纳入目标函数,以调节相位估计。为了验证我们的方法,我们使用了人类大脑数据集的小鼠心脏、小鼠肝脏和颞叶皮层。通过我们的实验,我们证明了我们提出的方法在预测昼夜节律基因的相位和揭示节律模式方面的有效性。
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
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