Cellular signal transduction occurs in complex and redundant interaction networks, which are best understood by simultaneously monitoring the activation dynamics of multiple components. Recent advances in biosensor technology have made it possible to visualize and quantify the activation of multiple network nodes in the same living cell. The precision and scope of this approach has been greatly extended by novel computational approaches (referred to as computational multiplexing) that can reveal relationships between network nodes imaged in separate cells.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3773929 | PMC |
| http://dx.doi.org/10.1038/nrm3212 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Revolutionizing load harmony in edge computing networks with probabilistic cellular automata and Markov decision processes.
Sci Rep
January 2025
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK.
In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP.
View Article and Find Full Text PDFA fair non-collateral consensus protocol based on Merkle tree for hierarchical IoT blockchain.
Sci Rep
January 2025
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
In recent years, there has been a growing interest among researchers in Internet of Things Blockchain (IoTB). A critical aspect of IoTB is its consensus protocol, which faces challenges such as limited bandwidth, energy constraints, and storage space restrictions. To tackle these challenges, Hierarchical IoTB (HIoTB) networks have been proposed.
View Article and Find Full Text PDFComprehensive computational analysis of differentially expressed miRNAs and their influence on transcriptomic signatures in prostate cancer.
Sci Rep
January 2025
Department of Pharmaceutics, College of Pharmacy, King Saud University, PO Box 2457, Riyadh, 11451, Saudi Arabia.
Prostate cancer presents a major health issue, with its progression influenced by intricate molecular factors. Notably, the interplay between miRNAs and changes in transcriptomic patterns is not fully understood. Our study seeks to bridge this knowledge gap, employing computational techniques to explore how miRNAs and transcriptomic alterations jointly regulate the development of prostate cancer.
View Article and Find Full Text PDFSimplified self-supervised learning for hybrid propagation graph-based recommendation.
Neural Netw
January 2025
School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.
View Article and Find Full Text PDFThe multifunctionality of the brainstem breathing control circuit.
Curr Opin Neurobiol
January 2025
Department of Physiology, University of California-San Francisco, San Francisco, CA 94143, USA.
Subconscious breathing is generated by a network of brainstem nodes with varying purposes, like pacing breathing or patterning a certain breath phase. Decades of anatomy, pharmacology, and physiology studies have identified and characterized the system's fundamental properties that produce robust breathing, and we now have well-conceived computational models of breathing that are based on the detailed descriptions of neuronal connectivity, biophysical properties, and functions in breathing. In total, we have a considerable understanding of the brainstem breathing control circuit.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!