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Extracting similar sub-graphs across PPI networks

Valeria Fionda, Luigi Palopoli, Simona Panni, Simona E. Rombo
2009 2009 24th International Symposium on Computer and Information Sciences  
Sub-graph extraction is carried out by taking into account, on the one side, both protein sequence and network structure similarities and, on the other side, both quantitative and reliability information  ...  This paper presents a method to discover highly matching sub-graphs in such networks.  ...  THE SUB-GRAPPIN ALGORITHM The final procedure SUB-GRAPPIN (LOcal search and COllapse based SUB-GRAph extraction through PPI Networks) implementing the proposed approach consists in interleaving calls of  ... 
doi:10.1109/iscis.2009.5291845 dblp:conf/iscis/FiondaPPR09 fatcat:6yifqtlfgzciva2o6pottm3rhy

Exploring Symmetric Substructures in Protein Interaction Networks for Pairwise Alignment [chapter]

Ahed Elmsallati, Swarup Roy, Jugal K. Kalita
2017 Lecture Notes in Computer Science  
We present a new pairwise aligner SSAlign (Symmetric Substructure Alignment) that extracts maximal substructures from participating PPI networks and uses Gene Ontology Consistency (GOC) as the graph isomorphic  ...  In molecular biology, comparison of multiple Protein Protein Interaction (PPI) networks to extract subnetworks that are conserved during evolution across di↵erent species is helpful for studying complex  ...  Comparing two biological networks is a challenging problem, since conserved sub-networks are expected to possess both topological and biological similarities.  ... 
doi:10.1007/978-3-319-56154-7_17 fatcat:akst5fyhangrnbpjq7tpdv5abq

Modelling the Yeast Interactome

Vuk Janjić, Roded Sharan, Nataša Pržulj
2014 Scientific Reports  
(human and yeast); (3) clear topological difference is present between PPI networks, their functional sub-modules, and their inter-functional "linkers"; (4) high confidence PPI networks have more "geometrical  ...  Yet, there is no definite agreement on the theoretical models which best describe protein-protein interaction (PPI) networks.  ...  To this end, we extract and model functional sub-networks of yeast PPI data.  ... 
doi:10.1038/srep04273 pmid:24589662 pmcid:PMC3940977 fatcat:uui47ek6incnjo6o4rcgcvzosy

DEN: AN R-BIOCONDUCTOR BASED PACKAGE TO EXTRACT ACTIVE SUB-NETWORKS FROM HUMAN INTERACTION MAP BY INTEGRATING GENE-EXPRESSION DATA

Sanket Shashikant Desai
2015 International Journal for Computational Biology  
DEN is an R/Bioconductor based package designed to assemble different types of human bio-molecular interactions as a complete interactome and contains functions to extract dynamic active networks by integration  ...  These interactions are analyzed in the form of static networks.  ...  The available sub-network extraction packages use either coregulated genes for network construction [25] or depend upon static PPI data [26, 27] .  ... 
doi:10.34040/ijcb.4.2.2015.60 fatcat:pfx4t26fvzdzrpktwxavdreihq

Systematic tracking of altered modules identifies the key biomarkers involved in chronic lymphocytic leukemia

Xin Lu, Zhen Wu, Xue‑Ying Zhao, Chun‑Feng Li, Shi‑Feng Kan
2018 Oncology Letters  
In this investigation, there were 1,805 and 703 modules in normal as well as disease PPI network.  ...  Additionally, we re-weighted the PPI network of normal and CLL conditions by means of Pearsons correlation coefficient (PCC).  ...  Herein, we used the clique-merging to extract the modules from PPI sub-networks.  ... 
doi:10.3892/ol.2018.9812 pmid:30675301 pmcid:PMC6341787 fatcat:o7wwq6zwsjgs7bubjp2kjwmfv4

Biological pathway completion using network motifs and random walks on graphs

Maya El Dayeh, Michael Hahsler
2012 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)  
In this paper, we extend our approach and use random walks on graphs to address the pathway completion problem with incomplete PPI networks.  ...  However, our previous approach requires a complete and correct PPI network.  ...  The algorithm accepts the weighted graph G (the probabilistic PPI network) and the directed sub-graph G' (the incomplete pathway).  ... 
doi:10.1109/cibcb.2012.6217235 dblp:conf/cibcb/DayehH12 fatcat:uw7za5csvzdpjogekos6wdm5ey

A Survey on Evolutionary Analysis in PPI Networks [chapter]

Pavol Jancura, Elena Marchiori
2012 Protein-Protein Interactions - Computational and Experimental Tools  
In the search phase, the alignment graph is turned into acyclic sub-graphs by random removal of alignment edges, which allows to extract high-scoring paths in linear time by a dynamic programming approach  ...  similarity in the respective PPI networks, global protein network alignment uses this mapping to define an unique, globally optimal mapping across whole topologies of PPI networks (Singh et al., 2007  ... 
doi:10.5772/37313 fatcat:t7airbrmjrd47l3qnv6fwjjplq

Structure discovery in PPI networks using pattern-based network decomposition

P. Bachman, Y. Liu
2009 Bioinformatics  
Results: In this article, we present an efficient algorithm for performing sub-graph isomorphism queries on a network and show its computational advantage over previous methods.  ...  This analysis provides support for the presence of hierarchical modularity in the PPI network of Saccharomyces cerevisiae. Contact: ying.liu@utdallas.edu  ...  Thus, in this section, we present a novel approach to using sub-graph queries that allows a PPI network to be decomposed into sub-networks exhibiting specific structural patterns.  ... 
doi:10.1093/bioinformatics/btp297 pmid:19447784 fatcat:u4y3tlyjbjcdfdn7aw7y2unhkm

A Survey of Graph Mining Techniques for Biological Datasets [chapter]

S. Parthasarathy, S. Tatikonda, D. Ucar
2010 Managing and Mining Graph Data  
Examples abound ranging from the analysis of protein interaction networks to the analysis of phylogenetic data.  ...  They extracted considerably large sub-pathways that are frequent across these organism-specific pathway graphs.  ...  In Section 3 we discuss algorithms that target the identification of frequent sub-patterns across multiple networks.  ... 
doi:10.1007/978-1-4419-6045-0_18 dblp:series/ads/ParthasarathyTU10 fatcat:aeu53r3dbzd67d5whkjypv64uq

Molecular Network Analysis and Applications [chapter]

Minlu Zhang, Jingyuan Deng, Chunsheng V. Fang, Xiao Zhang, Long Jason Lu
2010 Knowledge-Based Bioinformatics  
In order to extract modular structures from medium-and large-scale molec ular networks, for example PPI networks, various graph clustering approaches are adopted.  ...  score, which is a combination of sequence similarity and the likelihood that a PPI edge is true.  ... 
doi:10.1002/9780470669716.ch11 fatcat:dutvoyqsnbcgndejl2fpvo4o6a

Challenges and Limitations of Biological Network Analysis

Marianna Milano, Giuseppe Agapito, Mario Cannataro
2022 BioTech  
Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis.  ...  Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms' properties.  ...  The PNA applies a many-to-many node mapping among the input networks with the goal of finding similar sub-graphs.  ... 
doi:10.3390/biotech11030024 pmid:35892929 pmcid:PMC9326688 fatcat:7f5zp3sczzfclbbz6ty3nyremu

Predicting protein complexes by data integration of different types of interactions

Powell Patrick Cheng Tan, Daryanaz Dargahi, Frederic Pio
2010 International Journal of Computational Biology and Drug Design  
Here we propose a method that use graph theory and mathematical modelling to solve this problem.  ...  Our approach has four steps that i score independently each type of interaction ii build an interaction specific networks for each type iii Weight the specific networks and iv combine and normalise the  ...  The sub-graph of solved protein structures is weighted the most and the co-purification sub-graph the least. For each sub-graph, the pairwise PPI score is normalised using a z-score.  ... 
doi:10.1504/ijcbdd.2010.034464 pmid:20693608 fatcat:ps3pjt7na5e5pcxog3f4vxvxm4

Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information

Da Xu, Hanxiao Xu, Yusen Zhang, Wei Chen, Rui Gao
2020 Molecules  
In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE.  ...  Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and  ...  Overall, it indicates that our method can perform across species for PPI predictions.  ... 
doi:10.3390/molecules25081841 pmid:32316294 pmcid:PMC7221971 fatcat:rqonmvvkybhcroouwpjyppcknu

Mining functional subgraphs from cancer protein-protein interaction networks

Ru Shen, Nalin CW Goonesekere, Chittibabu Guda
2012 BMC Systems Biology  
Specifically, identification of similar and frequently occurring patterns (network motifs) across PPI networks will provide useful clues to better understand the biology of the diseases.  ...  Protein-protein interaction (PPI) networks carry vital information about proteins' functions.  ...  Using graph theory-based algorithms, pairs of networks can be compared to identify common, distinct or frequent sub-networks.  ... 
doi:10.1186/1752-0509-6-s3-s2 pmid:23282132 pmcid:PMC3524085 fatcat:t2yyillxyjb5beapv7jdysevma

A fast approach to global alignment of protein-protein interaction networks

Giorgos Kollias, Madan Sathe, Shahin Mohammadi, Ananth Grama
2013 BMC Research Notes  
Conclusions: We have demonstrated significant reductions in global network alignment computation times by coupling heuristic bipartite matching methods with the similarity scoring step of the IsoRank procedure  ...  These measures are quantified by the number of conserved edges in the alignment graph, the percentage of enriched components, and the total number of covered Gene Ontology (GO) terms.  ...  Specifically, if connected sub-graphs are conserved across species, they likely correspond to shared function across and within sub-graphs.  ... 
doi:10.1186/1756-0500-6-35 pmid:23363457 pmcid:PMC3672019 fatcat:hv232aurzfdj7gvdha7iadpl5e
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