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Relevant bibliographies by topics / Inferring PPI network

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Academic literature on the topic 'Inferring PPI network'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Inferring PPI network"

1

Jung, Dongmin, and Xijin Ge. "PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks." F1000Research 6 (November 7, 2017): 1969. http://dx.doi.org/10.12688/f1000research.12947.1.

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Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http:

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2

Jung, Dongmin, and Xijin Ge. "PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks." F1000Research 6 (December 8, 2017): 1969. http://dx.doi.org/10.12688/f1000research.12947.2.

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Abstract:

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http:

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3

Jung, Dongmin, and Xijin Ge. "PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks." F1000Research 6 (March 12, 2018): 1969. http://dx.doi.org/10.12688/f1000research.12947.3.

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Abstract:

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web si

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4

Modi, M., N. G. Jadeja, and K. Zala. "FMFinder: A Functional Module Detector for PPI Networks." Engineering, Technology & Applied Science Research 7, no. 5 (2017): 2022–25. https://doi.org/10.5281/zenodo.1037222.

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Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI) network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define

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5

Nguyen, Thanh-Phuong, Wei-chung Liu, and Ferenc Jordán. "Inferring pleiotropy by network analysis: linked diseases in the human PPI network." BMC Systems Biology 5, no. 1 (2011): 179. http://dx.doi.org/10.1186/1752-0509-5-179.

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6

SOHAEE, NASSIM, and CHRISTIAN V. FORST. "IDENTIFICATION OF FUNCTIONAL MODULES IN A PPI NETWORK BY BOUNDED DIAMETER CLUSTERING." Journal of Bioinformatics and Computational Biology 08, no. 06 (2010): 929–43. http://dx.doi.org/10.1142/s0219720010005221.

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Dense subgraphs of Protein–Protein Interaction (PPI) graphs are assumed to be potential functional modules and play an important role in inferring the functional behavior of proteins. Increasing amount of available PPI data implies a fast, accurate approach of biological complex identification. Therefore, there are different models and algorithms in identifying functional modules. This paper describes a new graph theoretic clustering algorithm that detects densely connected regions in a large PPI graph. The method is based on finding bounded diameter subgraphs around a seed node. The algorithm

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7

Barot, Meet, Vladimir Gligorijević, Kyunghyun Cho, and Richard Bonneau. "NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity." Bioinformatics 37, no. 16 (2021): 2414–22. http://dx.doi.org/10.1093/bioinformatics/btab098.

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Abstract Motivation Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer knowledge between species. These approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular context for meaningful prediction. To supply this context, network-based methods use protein-protein inte

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8

Tian, Bo, Qiong Duan, Can Zhao, Ben Teng, and Zengyou He. "Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 2 (2019): 365–76. http://dx.doi.org/10.1109/tcbb.2017.2705060.

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Sun, Wen, Lin Han, Wenmao Xu, and Yazhen Sun. "Identification of the Disrupted Pathways Associated with Periodontitis Based on Human Pathway Network." Infection International 5, no. 4 (2016): 93–98. http://dx.doi.org/10.1515/ii-2017-0143.

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AbstractObjective: The objective of this work is to search for a novel method to explore the disrupted pathways associated with periodontitis (PD) based on the network level.Methods: Firstly, the differential expression genes (DEGs) between PD patients and cognitively normal subjects were inferred based on LIMMA package. Then, the protein-protein interactions (PPI) in each pathway were explored by Empirical Bayesian (EB) co-expression program. Specifically, we determined the 100th weight value as the threshold value of the disrupted pathways of PPI by constructing the randomly model and confir

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10

Wang, Jiacheng, Jingpu Zhang, Yideng Cai, and Lei Deng. "DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model." International Journal of Molecular Sciences 20, no. 23 (2019): 6046. http://dx.doi.org/10.3390/ijms20236046.

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MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-prote

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Conference papers on the topic "Inferring PPI network"

1

Singh, Nitin, and M. Vidyasagar. "Inferring Gene Regulatory Networks with Sparse Bayesian Learning and phi-mixing coefficient." In 2014 European Control Conference (ECC). IEEE, 2014. http://dx.doi.org/10.1109/ecc.2014.6862185.

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2

Singh, Nitin, Mehmet Eren Ahsen, Shiva Mankala, M. Vidyasagar, and Michael White. "Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient." In 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2012. http://dx.doi.org/10.1109/gensips.2012.6507755.

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