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The structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several…

统计力学 · 物理学 2007-05-23 Johannes Berg , Michael Lässig , Andreas Wagner

PPI network alignment aims to find topological and functional similarities between networks of different species. Several alignment approaches have been proposed. Each of these approaches relies on a different alignment method and uses…

定量方法 · 定量生物学 2019-11-25 Sarra Ghanjeti

Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have a correct…

分子网络 · 定量生物学 2007-05-23 Natasa Przulj , Derek G. Corneil , Igor Jurisica

Proteins interact with other proteins within biological pathways, forming connected subgraphs in the protein-protein interactome (PPI). Proteins are often involved in multiple biological pathways which complicates interpretation of…

分子网络 · 定量生物学 2012-12-04 Sira Sriswasdi , Shane T. Jensen

It has recently been demonstrated that many biological networks exhibit a scale-free topology where the probability of observing a node with a certain number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has been…

分子网络 · 定量生物学 2009-11-11 Eric J. Deeds , Orr Ashenberg , Eugene I. Shakhnovich

We employed the random graph theory approach to analyze the protein-protein interaction database DIP (Feb. 2004), for seven species (S. cerevisiae, H. pylori, E. coli, C. elegans, H. sapiens, M. musculus and D. melanogaster). Several global…

分子网络 · 定量生物学 2007-05-23 Ka-Lok Ng , Chien-Hung Huang

Understanding of how protein interaction networks (PIN) of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce a hybrid network model composed…

分子网络 · 定量生物学 2007-05-23 K. -I. Goh , B. Kahng , D. Kim

Protein-protein interaction (PPI) networks, providing a comprehensive landscape of protein interacting patterns, enable us to explore biological processes and cellular components at multiple resolutions. For a biological process, a number…

分子网络 · 定量生物学 2016-04-13 Xiuli Ma , Guangyu Zhou , Jingjing Wang , Jian Peng , Jiawei Han

Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the…

生物大分子 · 定量生物学 2024-02-07 Chenqing Hua , Connor Coley , Guy Wolf , Doina Precup , Shuangjia Zheng

Understanding the mathematical properties of graphs underling biological systems could give hints on the evolutionary mechanisms behind these structures. In this article we perform a complete statistical analysis over thousands of graphs…

分子网络 · 定量生物学 2019-08-29 D. Gamermann , J. Triana , R. Jaime

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional…

分子网络 · 定量生物学 2023-10-05 Rodrigo Henrique Ramos , Cynthia de Oliveira Lage Ferreira , Adenilso Simao

How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous non-functional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are…

生物大分子 · 定量生物学 2011-01-04 Muyoung Heo , Sergei Maslov , Eugene I. Shakhnovich

Aligning protein-protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help…

最优化与控制 · 数学 2009-05-08 Mikhail Zaslavskiy , Francis Bach , Jean-Philippe Vert

Background. Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene…

分子网络 · 定量生物学 2020-04-28 Qi Li , Tijana Milenković

Biological data are extremely diverse, complex but also quite sparse. The recent developments in deep learning methods are offering new possibilities for the analysis of complex data. However, it is easy to be get a deep learning model that…

机器学习 · 计算机科学 2019-01-21 Florian Richoux , Charlène Servantie , Cynthia Borès , Stéphane Téletchéa

Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and…

分子网络 · 定量生物学 2017-12-05 Monica Agrawal , Marinka Zitnik , Jure Leskovec

Biological networks provide insight into the complex organization of biological processes in a cell at the system level. They are an effective tool for understanding the comprehensive map of functional interactions, finding the functional…

分子网络 · 定量生物学 2017-09-14 Somaye Hashemifar

The evolution of complex networks is governed by both growing rules and internal properties. Most evolving network models (e.g. preferential attachment) emphasize on the growing strategy, while neglecting the characteristics of individual…

社会与信息网络 · 计算机科学 2020-05-07 Dong Chen , Hong Yu

From the spectral plot of the (normalized) graph Laplacian, the essential qualitative properties of a network can be simultaneously deduced. Given a class of empirical networks, reconstruction schemes for elucidating the evolutionary…

定量方法 · 定量生物学 2007-05-24 Anirban Banerjee , Jürgen Jost

We study the correlation between the codon usage bias of genetic sequences and the network features of protein-protein interaction (PPI) in bacterial species. We use PCA techniques in the space of codon bias indices to show that genes with…

基因组学 · 定量生物学 2021-02-23 Maddalena Dilucca , Giulio Cimini , Sergio Forcelloni , Andrea Giansanti