This file contains the protein-protein interaction analysis dataset that was used in the unpublished manuscript and was further analyzed with the STRING online software.Significantly upregulated mRNAs (2,777 genes; p < 0.05) identified by bulk RNA-seq were analyzed using the STRING module in Cytoscape v.2.2.0 (Institute for System Biology; WA; USA). A cluster network was constructed using the MCL algorithm with a granularity parameter of 4, followed by filtering nodes with mcl.cluster > 10. The resulting 1,848 nodes were processed through STRING v12.0 (Swiss Institute of Bioinformatics; Lausanne; Switzerland) to generate a protein–protein interaction (PPI) network, incorporating evidence from text mining, genomic neighborhood, experimental data, curated databases, co-expression, gene fusion, and co-occurrence, with a minimum confidence score threshold of 0.40. Network modules were defined using the DBSCAN clustering algorithm with an ε parameter of 2. Cluster 1, representing the largest gene set (101 genes), was further analyzed by sorting the top 20 nodes with the highest node degree, resulting in a network comprising 101 nodes and 756 edges. Global network metrics indicated an average node degree of 15, a local clustering coefficient of 0.600, and a PPI enrichment p-value of < 1 × 10⁻¹⁶. The average values of coexpression, experimentally determined interactions, automated text mining, and combined scores were calculated.

STRING protein-protein interaction networks for WT-C vs. WT-D.

Validation of the total new predicted links and the new predicted links associated with the 10 proteins by STRING database for the 14317_PPI data.

Statistics of the genes in the protein interaction network constructed based on the STRING database.

Column ‘Gene’ contains the T. reesei gene ID. ‘In STRING’ tells if the gene has interactions in STRING. Columns ‘Btw’ and ‘Deg’ denote the betweenness and degree network statistics of the corresponding gene. Columns ‘Class’ and ‘Putative secretion pathway component’ are author assigned classifications. ‘Taxon specificity’ gives the largest taxonomic group the gene was found in.

Basic information of the four original networks (HIPPIE, HumanNet, FunCoup and STRING) and the GO network.

Summary statistics for protein-protein interaction networks identified with STRING amongst genes corresponding to significant SNPs or k-mers (inside or adjacent to genes). PPI enrichment p-value corresponds to the likelihood nodes and edges would be selected from the S. aureus database by chance.

Data description

This data note describes the final citation network dataset analysed in the manuscript "What is co-production? Conceptualising and understanding co-production of knowledge and policy across different theoretical perspectives’"[1].

The data collection strategy used to construct the following dataset can be found in the associated manuscript [1]. These data were originally downloaded from the Web of Science (WoS) Core Collection via the library subscription of the University of Edinburgh via a systematic search methodology that sought to capture literature relevant to ‘knowledge co-production’. The dataset consists of 1,893 unique document reference strings (nodes) interlinked together by 9,759 citation links (edges). The network dataset describes a directed citation network composed of papers relevant to 'knowledge co-production', and is split into two files: (i) ‘KnowCo_node_attribute_list.csv’ contains attributes of the 1,893 documents (nodes); and (ii) ‘KnowCo_edge_list.csv’ records the citation links (edges) between pairs of documents.

1. ‘KnowCo_node_attribute_list.csv’ consists of attributes of the 1,893 nodes (documents) of the citation network. Due to the approach used to collect data, there are two types of node: (i) 525 nodes represent documents retrieved from WoS via the systematic search strategy, and these have full attribute data including their reference lists; and (ii) 1,368 documents that were cited >2 times by our 525 fully retrieved papers (see manuscript for full description [1]). The columns refer to:

Id, the unique identifier. Fully retrieved documents are identified via a unique identifier that begins with ‘f’ followed by an integer (e.g. f1, f2, etc.). Non-retrieved documents are identified via a unique identifier beginning with ‘n’ followed by an integer (e.g. n1, n2, etc.).

Label, contains the unique reference string of the document for which the attribute data in that row corresponds. Reference strings contain the last name of the first author, publication year, journal, volume, start page, and DOI (if available).

authors, all author names. These are in the order that these names appear in the authorship list of the corresponding document. These data are only available for fully retrieved documents.

title, document title. These data are only available for fully retrieved documents.

journal, journal of publication. These data are only available for fully retrieved documents. For those interested in journal data for the remaining papers, this can be extracted from the reference string in the ‘Label’ column.

year, year of publication. These data are available for all nodes.

type, document type (e.g. article, review). Available only for fully retrieved documents.

wos_total_citations, total citation count as recorded by Web of Science Core Collection as of May 2020. Available only for fully retrieved documents.

wos_id, Web of Science accession number. Available only for fully retrieved documents only, for non-retrieved documents ‘CitedReference’ fills the cell.

cluster, provides the cluster membership number as discussed within the manuscript, established via modularity maximisation via the Leiden algorithm (Res 0.8; Q=0.53|5 clusters). Available for all nodes.

indegree, total count of within network citations to a given document. Due to the composition of the network, this figure tells us the total number of citations from 525 fully retrieved documents to each of the 1,893 documents within the network. Available for all nodes.

outdegree, total count of within network references from a given document. Due to the composition of the network, only fully retrieved documents can have a value >0 because only these documents have their associated reference list data. Available for all nodes.

2. ‘KnowCo_edge _list.csv’ is an edge list containing 9,759 citation links between the 1,893 documents. The columns refer to:

Source, the citing document’s unique identifier.

Target, the cited document’s unique identifier.

Notes

[1] Bandola-Gill, J., Arthur, M., & Leng, R. I. (Under review). What is co-production? Conceptualising and understanding co-production of knowledge and policy across different theoretical perspectives. Evidence & Policy

Protein–protein interaction network of the top differentially expressed genes between the patient’s samples and the Ctrl cohort. Edges represent protein–protein associations. Confidence ≥0.700; maximum number of interactors ≤20. Edge confidence: high (0.700) and highest (0.900) (see https://string-db.org/cgi/network).

Results on scGEM with ground truth determined by (1) cell-type specific networks validated by experimental data [44] and (2) STRING [45]. The random baseline for AUPRC is 0.020 for the experimental network and 0.058 for the STRING network.

Selection of 30 central genes from PPI network, including 17 upregulated and 13 downregulated genes, by using the STRING and Cytoscape software.

The prediction of protein complexes from protein-protein interactions (PPIs) is a well-studied problem in bioinformatics. However, the currently available PPI data is not enough to describe all known protein complexes. In this paper, we express the problem of determining the minimum number of (additional) required protein-protein interactions as a graph theoretic problem under the constraint that each complex constitutes a connected component in a PPI network. For this problem, we develop two computational methods: one is based on integer linear programming (ILPMinPPI) and the other one is based on an existing greedy-type approximation algorithm (GreedyMinPPI) originally developed in the context of communication and social networks. Since the former method is only applicable to datasets of small size, we apply the latter method to a combination of the CYC2008 protein complex dataset and each of eight PPI datasets (STRING, MINT, BioGRID, IntAct, DIP, BIND, WI-PHI, iRefIndex). The results...

A history and project summary of the development of a very high-speed drill string communications network are given. The summary includes laboratory and field test results, including recent successes of the system in wells in Oklahoma. A brief explanation of commercialization plans is included. The primary conclusion for this work is that a high data rate communications system can be made sufficiently robust, reliable, and transparent to the end user to be successfully deployed in a down-hole drilling environment. A secondary conclusion is that a networking system with user data bandwidth of at least 1 million bits per second can be built to service any practical depth of well using multiple repeaters (Links), with spacing between the Links of at least 1000 ft.

Summary of GO term-centric results obtained by different network embedding representations and corresponding functional representations based on Combinedscore, Textmining, Experimental, Database and Coexpression networks working with different classification algorithms during hold-out evaluation.

The database of protein-chemical structural interactions includes all existing 3D structures of complexes of proteins with low molecular weight ligands. When one considers the proteins and chemical vertices of a graph, all these interactions form a network. Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The current version includes data from the Protein Data Bank as of August 2011. The database is updated monthly.

Purpose Identification of potential biomarkers of seizures. Methods In this exploratory study, we quantified plasma protein intensities in 15 patients with recent seizures compared to 15 patients with long-standing seizure freedom. Using TMT-based proteomics we found fifty-one differentially expressed proteins. Results Network analyses including co-expression networks and protein-protein interaction networks, using the STRING database, followed by network centrality and modularity analyses revealed 22 protein modules, with one module showing a significant association with seizures. The protein-protein interaction network centered around this module identified a subnetwork of 125 proteins, grouped into four clusters. Notably, one cluster (mainly enriching inflammatory pathways and Gene Ontology terms) demonstrated the highest enrichment of known epilepsy-related genes. Conclusion Overall, our network-based approach identified a protein module linked with seizures. The module contained known markers of epilepsy and inflammation. The results also demonstrate the potential of network analysis in discovering new biomarkers for improved epilepsy management.

Homophily/heterophily evaluation, expressed in terms of z-score values, is related to the human Protein-Protein Interaction Network (PPI), obtained from the STRING v11.5 database (https://string-db.org) setting standard threshold on edge score (T=700). Each protein occurring in the PPI was assigned to a class corresponding to the chromosome the related gene belongs to.

A total of 23 classes (chr1, chr2, ..., chr22, chrX) were considered (excluding the class corresponding to chromosome Y because of the small number of genes occurring in the network).

The homophily/heterophily nature of the network, with respect to chromosome classes, was evaluated through HONTO tool (https://github.com/cumbof/honto).

In other words, the tendency of proteins to preferentially interact with proteins whose genes are physically located on the same chromosome (homophily) or on different chromosomes (heterophily) was investigated and evaluated in terms of z-scores.

Values related to intra (along the diagonal) and inter chromosomal interactions (other than the diagonal) are also reported as a heatmap.

As one can observe, values occurring in the diagonal are clearly higher than values out of the diagonal, leading to assess a homophilic nature of the network, confirming the link between shared chromosome and interaction in the PPI.

All human protein interactions were obtained from STRING (https://string-db.org/, version 11.0). Interactions were then filtered to those involving only BM zone proteins. Related to Fig. S6B.

Network of 45 papers and 108 citation links related to "Russian doll spectrum in a non-Abelian string-net ladder".

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.