WeChat Mini Program
Old Version Features

Finding Conserved Patterns in Multilayer Networks.

Bio-inspired Human-Machine Interfaces and Healthcare Applications (B-Interface)(2019)

Univ Florida

Cited 5|Views45
Abstract
Motivation: Traditional methods often represent complex systems as a single, static, and binary network. These models are inadequate in capturing complex cellular interactions which vary under different conditions as well as over time. Furthermore the same set of molecules can interact in varying patterns across different interactomes. In this paper, we model cellular interactions as a set of network topologies, called multilayer networks. We consider motif counting, one of the most fundamental problems in network analysis. Existing motif counting and identification methods are limited to single network topologies, and thus they cannot be directly applied on multilayer networks. Results: In this paper, we extend the classical network motif identification problem to multilayer networks. We develop an efficient and accurate method to solve this problem. Our results on Escherichia coli (E.coli) transcription regulatory network under different experimental conditions show that our method scales to real networks and more importantly can uncover conserved functional characteristics of genes participating in the network under various conditions with very low false discovery rates.
More
Translated text
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文针对传统方法无法充分捕捉复杂细胞交互的问题,提出了在多层网络中寻找保守模式的新方法,并在大肠杆菌转录调节网络中验证了其有效性和准确性。

方法】:通过将细胞交互建模为多层网络,并扩展了传统的网络模式识别问题到多层网络中,开发了一种高效精确的解决方法。

实验】:在Escherichia coli(E.coli)转录调节网络的不同实验条件下,使用该方法,结果表明它能够扩展到真实网络,并能以很低的假发现率揭示基因在网络中不同条件下的保守功能特征。