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Journal articles on the topic 'Inferring PPI network'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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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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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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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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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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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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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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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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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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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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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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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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Hu, Baofang, Hong Wang, Lutong Wang, and Weihua Yuan. "Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach." Molecules 23, no. 12 (2018): 3193. http://dx.doi.org/10.3390/molecules23123193.

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Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of the prediction methods mainly rely on direct similarities inferred from drug information and cannot fully utilize the drug information about the impact of protein–protein interactions (PPI) on potential drug targets. Moreover, most of the methods are designed for specific tasks. In th

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WANG, JINGYAN, and YONGPING LI. "SEQUENTIAL LINEAR NEIGHBORHOOD PROPAGATION FOR SEMI-SUPERVISED PROTEIN FUNCTION PREDICTION." Journal of Bioinformatics and Computational Biology 09, no. 06 (2011): 663–79. http://dx.doi.org/10.1142/s0219720011005550.

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Predicting protein function is one of the most challenging problems of the post-genomic era. The development of experimental methods for genome scale analysis of molecular interaction networks has provided new approaches to inferring protein function. In this paper we introduce a new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP), which addresses the problem of the classification of partially labeled protein interaction networks. The proposed SLNP first constructs a sequence of node sets according to their shortest distance to the labeled

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Zhang, Tianjiao, Zhenao Wu, Liangyu Li, et al. "CellGAT: A GAT-Based Method for Constructing a Cell Communication Network Integrating Multiomics Information." Biomolecules 15, no. 3 (2025): 342. https://doi.org/10.3390/biom15030342.

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The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of protein–protein interactions, including ligand–receptor; receptor–receptor, and extracellular matrix–receptor interactions. Currently, computational methods for inferring ligand–receptor communication primarily depend on gene expression data of ligand–receptor pairs and spatial information of cells. Some approaches integrate protein complexes; tra

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Du, Yang, Meng Cai, Xiaofang Xing, Jiafu Ji, Ence Yang, and Jianmin Wu. "PINA 3.0: mining cancer interactome." Nucleic Acids Research 49, no. D1 (2020): D1351—D1357. http://dx.doi.org/10.1093/nar/gkaa1075.

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Abstract Protein–protein interactions (PPIs) are crucial to mediate biological functions, and understanding PPIs in cancer type-specific context could help decipher the underlying molecular mechanisms of tumorigenesis and identify potential therapeutic options. Therefore, we update the Protein Interaction Network Analysis (PINA) platform to version 3.0, to integrate the unified human interactome with RNA-seq transcriptomes and mass spectrometry-based proteomes across tens of cancer types. A number of new analytical utilities were developed to help characterize the cancer context for a PPI netw

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ANGEL, SERGIO O., MARIA J. FIGUERAS, MARIA L. ALOMAR, PABLO C. ECHEVERRIA, and BIN DENG. "Toxoplasma gondiiHsp90: potential roles in essential cellular processes of the parasite." Parasitology 141, no. 9 (2014): 1138–47. http://dx.doi.org/10.1017/s0031182014000055.

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SUMMARYHsp90 is a widely distributed and highly conserved molecular chaperone that is ubiquitously expressed throughout nature, being one of the most abundant proteins within non-stressed cells. This chaperone is up-regulated following stressful events and has been involved in many cellular processes. InToxoplasma gondii, Hsp90 could be linked with many essential processes of the parasite such as host cell invasion, replication and tachyzoite-bradyzoite interconversion. A Protein-Protein Interaction (PPI) network approach of TgHsp90 has allowed inferring how these processes may be altered. In

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Huang, Xiaoqing, Aamir R. Hullur, Elham Jafari, et al. "Leveraging transcription factor physical proximity for enhancing gene regulation inference." Bioinformatics 41, Supplement_1 (2025): i533—i541. https://doi.org/10.1093/bioinformatics/btaf186.

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Abstract Motivation Gene regulation inference, a key challenge in systems biology, is crucial for understanding cell function, as it governs processes such as differentiation, cell state maintenance, signal transduction, and stress response. Leading methods utilize gene expression, chromatin accessibility, transcription factor (TF) DNA binding motifs, and prior knowledge. However, they overlook the fact that TFs must be in physical proximity to facilitate transcriptional gene regulation. Results To fill the gap, we develop GRIP—Gene Regulation Inference by considering TF Proximity—a gene regul

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Bhardwaj, Anuradha, Ahmad Obaid, Anmar Anwar Khan, et al. "Inferring microarray datasets reveals critical biomarkers and potential drug targets of Parkinson’s disease." Neurology Asia 29, no. 4 (2024): 1053–61. https://doi.org/10.54029/2024stx.

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Parkinson’s disease (PD) is a critical neurological disorder characterized by loss of voluntary motor control and substantial slowing of movement. While traditionally attributed to environmental factors, recent studies underscore the significant role of genetics in the onset and progression of PD. This study aimed to identify differentially expressed genes (DEGs) and relevant pathways in PD by analyzing gene expression data from four datasets (83 PD and 53 control substantia nigra samples) sourced from the Gene Expression Omnibus (GEO) database. Using GEO2R, we identified common DEGs and perfo

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Noor, Amina, Erchin Serpedin, Mohamed Nounou, Hazem Nounou, Nady Mohamed, and Lotfi Chouchane. "An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks." Advances in Bioinformatics 2013 (February 21, 2013): 1–12. http://dx.doi.org/10.1155/2013/953814.

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The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It a

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Browne, Fiona, Haiying Wang, Huiru Zheng, and Francisco Azuaje. "An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions." Journal of Integrative Bioinformatics 3, no. 2 (2006): 230–46. http://dx.doi.org/10.1515/jib-2006-41.

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Abstract Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive

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Alborzi, Seyed Ziaeddin, Amina Ahmed Nacer, Hiba Najjar, David W. Ritchie, and Marie-Dominique Devignes. "PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions." PLOS Computational Biology 17, no. 8 (2021): e1008844. http://dx.doi.org/10.1371/journal.pcbi.1008844.

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Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational

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Chen, Yile, Xiucheng Li, Gao Cong, et al. "Points-of-interest relationship inference with spatial-enriched graph neural networks." Proceedings of the VLDB Endowment 15, no. 3 (2021): 504–12. http://dx.doi.org/10.14778/3494124.3494134.

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As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxo

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Cui, Yue, Hao Sun, Yan Zhao, Hongzhi Yin, and Kai Zheng. "Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach." ACM Transactions on Information Systems 40, no. 2 (2022): 1–22. http://dx.doi.org/10.1145/3460198.

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Accurately recommending the next point of interest (POI) has become a fundamental problem with the rapid growth of location-based social networks. However, sparse, imbalanced check-in data and diverse user check-in patterns pose severe challenges for POI recommendation tasks. Knowledge-aware models are known to be primary in leveraging these problems. However, as most knowledge graphs are constructed statically, sequential information is yet integrated. In this work, we propose a meta-learned sequential-knowledge-aware recommender (Meta-SKR), which utilizes sequential, spatio-temporal, and soc

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Singh, Nitin, Mehmet Eren Ahsen, Niharika Challapalli, Hyun-Seok Kim, Michael A. White, and Mathukumalli Vidyasagar. "Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer." IEEE Transactions on Molecular, Biological and Multi-Scale Communications 4, no. 3 (2018): 123–39. http://dx.doi.org/10.1109/tmbmc.2019.2933391.

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Meng, Zixuan, Linai Kuang, Zhiping Chen, et al. "Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network." Frontiers in Genetics 12 (March 17, 2021). http://dx.doi.org/10.3389/fgene.2021.645932.

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In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expressio

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DJEDDI, Warith Eddine. "TANA: efficient approach for predicting protein functions from sequence homology and alignment of protein-protein interaction networks." March 19, 2020. https://doi.org/10.5281/zenodo.4165601.

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TANA is a novel approach for inferring protein functions by combining three complementary pipelines from sequence homology-based annotation and PPI network alignment. The main originality of the introduced approach stands on the combination using the logistic regression method. The latter combines the function prediction for the unannotated protein by transferring annotation via the PPI network alignment and from the sequence homology using BLAST and PSI-BLAST.

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Perna, Stefano, Pietro Pinoli, Stefano Ceri, and Limsoon Wong. "NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information." Biology Direct 15, no. 1 (2020). http://dx.doi.org/10.1186/s13062-020-00268-1.

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Abstract Background Inferring the mechanisms that drive transcriptional regulation is of great interest to biologists. Generally, methods that predict physical interactions between transcription factors (TFs) based on positional information of their binding sites (e.g. chromatin immunoprecipitation followed by sequencing (ChIP-Seq) experiments) cannot distinguish between different kinds of interaction at the same binding spots, such as co-operation and competition. Results In this work, we present the Network-Augmented Transcriptional Interaction and Coregulation Analyser (NAUTICA), which empl

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Hsieh, Kang-Lin, Kai Zhang, Yan Chu, et al. "iGTP: learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics." Briefings in Bioinformatics 26, no. 3 (2025). https://doi.org/10.1093/bib/bbaf296.

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Abstract Deep-learning models like Variational AutoEncoder have enabled low dimensional cellular embedding representation for large-scale single-cell transcriptomes and shown great flexibility in downstream tasks. However, biologically meaningful latent space is usually missing if no specific structure is designed. Here, we engineered a novel interpretable generative transcriptional program (iGTP) framework that could model the importance of transcriptional program (TP) space and protein–protein interactions (PPI) between different biological states. We demonstrated the performance of iGTP in

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Pirak, Daniel, and Roded Sharan. "D’or: Deep orienter of protein-protein interaction networks." Bioinformatics, June 11, 2024. http://dx.doi.org/10.1093/bioinformatics/btae355.

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Abstract Motivation Protein-protein interactions (PPIs) provide the skeleton for signal transduction in the cell. Current PPI measurement techniques do not provide information on their directionality which is critical for elucidating signaling pathways. To date, there are hundreds of thousands of known PPIs in public databases, yet only a small fraction of them have an assigned direction. This information gap calls for computational approaches for inferring the directionality of PPIs, aka network orientation. Results In this work we propose a novel deep learning approach for PPI network orient

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Zhang, Yan, Ju Xiang, Liang Tang, et al. "Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity." Frontiers in Genetics 12 (August 16, 2021). http://dx.doi.org/10.3389/fgene.2021.596794.

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Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Onl

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Acquisti, Alessandro, Ralph Gross, and Fred Stutzman. "Face Recognition and Privacy in the Age of Augmented Reality." Journal of Privacy and Confidentiality 6, no. 2 (2014). http://dx.doi.org/10.29012/jpc.v6i2.638.

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We investigate the feasibility of combining publicly available Web 2.0 data with off-the-shelf face recognition software for the purpose of large-scale, automated individual re-identification. Two experiments illustrate the ability of identifying strangers online (on a dating site where individuals protect their identities by using pseudonyms) and offline (in a public space), based on photos made publicly available on a social network site. A third proof-of-concept experiment illustrates the ability of inferring strangers' personal or sensitive information (their interests and Social Security

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31

Gao, Junning, Lizhi Liu, Shuwei Yao, Xiaodi Huang, Hiroshi Mamitsuka, and Shanfeng Zhu. "HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks." BMC Medical Genomics 12, S10 (2019). http://dx.doi.org/10.1186/s12920-019-0625-1.

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Abstract Background As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. Method For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and

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Nilsen, André Sevenius, Alessandro Arena, and Johan F. Storm. "Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG." Neuroscience of Consciousness 2024, no. 1 (2024). http://dx.doi.org/10.1093/nc/niae021.

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Abstract To investigate mechanisms underlying loss of consciousness, it is important to extend methods established in humans to rodents as well. Perturbational complexity index (PCI) is a promising metric of “capacity for consciousness” and is based on a perturbational approach that allows inferring a system’s capacity for causal integration and differentiation of information. These properties have been proposed as necessary for conscious systems. Measures based on spontaneous electroencephalography recordings, however, may be more practical for certain clinical purposes and may better reflect

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Nair, Surag, Avanti Shrikumar, Jacob Schreiber, and Anshul Kundaje. "fastISM: performant in silico saturation mutagenesis for convolutional neural networks." Bioinformatics, March 3, 2022. http://dx.doi.org/10.1093/bioinformatics/btac135.

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Abstract Motivation Deep-learning models, such as convolutional neural networks, are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In silico saturation mutagenesis (ISM) is a popular feature attribution technique for inferring contributions of all characters in an input sequence to the model’s predicted output. The main drawback of ISM is its runtime, as it involves multiple forward propagations of all possible mutations of each character in the input sequence through the trained model to predict the

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Okada, K., K. K. Kusu, S. K. Kikuchi, et al. "Prognostic prediction of cardiovascular adverse events in patients after percutaneous coronary intervention using machine learning." European Heart Journal 45, Supplement_1 (2024). http://dx.doi.org/10.1093/eurheartj/ehae666.1312.

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Abstract Introduction While prevention of major cardiovascular adverse events (MACE) is important in the management of patients after percutaneous coronary intervention (PCI), risk stratification of MACE remains a significant challenge with increasingly diverse and complex patient backgrounds. In response to that, this retrospective observational study aimed to explore whether machine learning (ML) methods can predict future MACE after PCI using a variety of features extracted from electronic medical records (EMRs). Methods In 3,016 patients with coronary artery disease who performed PCI, 3 ma

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