Complex networks & their applications. International Conference on Complex Networks and Their Applications最新文献

筛选
英文 中文
Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation. 基于网络的空间转录组学数据的自编码器去噪及其在细胞信号估计中的应用。
Complex networks & their applications. International Conference on Complex Networks and Their Applications Pub Date : 2026-01-01 Epub Date: 2026-05-01 DOI: 10.1007/978-3-032-16719-4_16
Azka Javaid, H Robert Frost
{"title":"Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation.","authors":"Azka Javaid, H Robert Frost","doi":"10.1007/978-3-032-16719-4_16","DOIUrl":"10.1007/978-3-032-16719-4_16","url":null,"abstract":"<p><p>We propose an autoencoder-based framework for denoising networks estimated from Spatial Transcriptomics (ST) data for cell signaling analysis. Our method consists of an unsupervised encoder-decoder framework for denoising the network adjacency matrix and a supervised framework for cell signaling estimation. We validate our denoising component using the Frobenius norm metric for graphs simulated using the Barabási-Albert (BA) and Erdős-Rényi (ER) models against reconstructions generated using the singular value decomposition (SVD). We then validate the cell signaling estimates generated using the supervised component on real ST data for the Wnt3-Fzd1 and Ephb1-Efnb3 interactions. We report that our framework achieves better adjacency matrix reconstructions for superlinear BA and dense ER graphs and generates cell signaling estimates that are both regionally specific and biologically plausible. An important contribution of this work is the application of neural networks for network-based cell signaling estimation using ST data and the benchmarking of autoencoder versus SVD denoising for different graph models.</p>","PeriodicalId":523858,"journal":{"name":"Complex networks & their applications. International Conference on Complex Networks and Their Applications","volume":"1266 ","pages":"196-207"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13191374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148020547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Eigenvector centrality for multilayer networks with dependent node importance. 具有相关节点重要性的多层网络的特征向量中心性。
Complex networks & their applications. International Conference on Complex Networks and Their Applications Pub Date : 2024-01-01 Epub Date: 2024-02-21 DOI: 10.1007/978-3-031-53472-0_1
H Robert Frost
{"title":"Eigenvector centrality for multilayer networks with dependent node importance.","authors":"H Robert Frost","doi":"10.1007/978-3-031-53472-0_1","DOIUrl":"10.1007/978-3-031-53472-0_1","url":null,"abstract":"<p><p>We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. An R package implementing this method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/.</p>","PeriodicalId":523858,"journal":{"name":"Complex networks & their applications. International Conference on Complex Networks and Their Applications","volume":"1143 ","pages":"3-14"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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×
相关产品
×
本文献相关产品
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:604180095
Book学术官方微信
小红书