ataset representing a Protein-Protein Interaction (PPI) network of human proteins. Data generated and scored using the comprehensive STRING database resource. Focuses on analyzing functional and physical associations between proteins. Includes confidence scores (e.g., text-mining, experimental) for each interaction. A foundational resource for systems biology and identifying molecular hubs in disease pathways.

This file contains the protein-protein interaction analysis dataset that was used in the unpublished manuscript and was further analyzed with the STRING online software.Significantly upregulated mRNAs (2,777 genes; p 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.

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.

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

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

Data for RAPPPID, a method for the Regularised Automative Prediction of Protein-Protein Interactions using Deep Learning. These datasets are in a format that RAPPPID is ready to read. Comparatives Dataset These datasets were derived from the STRING v11 H. sapiens dataset, according to the C1, C2, and C3 procedures outlined by Park and Marcotte, 2012. Negative samples are sampled randomly from the space of proteins not known to interact. See Szymborski & Emad for details. Repeatability Datasets The following datasets are all derived from STRING in the manner as the comparatives dataset, but three different random seeds are used for drawing proteins. References Park,Y. and Marcotte,E.M. (2012) Flaws in evaluation schemes for pair-input computational predictions. Nat Methods, 9, 1134–1136. Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., and Mering, C. (2019). String v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613. Szymborski,J. and Emad,A. (2021) RAPPPID: Towards Generalisable Protein Interaction Prediction with AWD-LSTM Twin Networks. bioRxiv https://doi.org/10.1101/2021.08.13.456309

Description:

The European Power Grid Network dataset contains anonym zed data that sheds light on the intricate connections between nodes within Europe’s electricity grid. Researchers and policymakers can leverage this dataset to gain valuable insights into energy trading patterns, nodal prices, and the stability of energy supply.

1. Network Structure and Insights:

o The dataset provides detailed information about the interconnections between nodes across the European power grid. Researchers can analyze these links to understand how electricity flows between different regions. o By examining nodal prices, researchers can uncover pricing dynamics. This includes variations based on geographical location, demand, and supply. o Geospatial analysis facilitated by this dataset allows researchers to identify patterns in power market behavior, congestion points, and reliability challenges.

2. Critical Energy Supplies and Stability:

o Identifying critical energy supplies is essential for maintaining grid stability. Policymakers can use this dataset to inform decisions related to energy security and resilience. o Additionally, the dataset enables cross-state comparisons of power price competitiveness, aiding policymakers in designing effective energy policies.

This dataset contains anonymized information about the European power grid network, providing insights on the connections between nodes and their pricing. To use this dataset, one must identify the source and destination nodes of the power grid along with associated features such as prices and country information.

Firstly, it is important to understand the readings of each column in order to navigate through the data effectively:

1. from: The source node of the power grid. (Integer)

2. to: The destination node of the power grid. (Integer)

3. name: Name of the node in European Power Grid Network. (String)

4. price: Price of electricity at each node. (Float)

5. country: Country in which a particular node is located. (String).

Secondly, it is helpful to visualize and explore this dataset with various plots for better understanding its features for valuable analysis insights such as geospatial exploration by plotting out their geographical locations on maps; comparison between different countries or regions regarding electricity prices; assessing economic relationships through trade flows or supply-chains networks related to energy market developments; etc., all are possible via simple analyses that can be done from this european_power_grid dataset!

Acknowledgements

If you use this dataset in your research, please credit the original authors.

https://zenodo.org/records/7037956#.Y9Y6yNJBwUE

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

https://creativecommons.org/publicdomain/zero/1.0/

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

STRING is a database of known and predicted protein interactions, including both physical and functional interactions. It contains data which derived from four sources: genomic context, high-throughput experiments, coexpression and previous knowledge. This database quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. It performs iterative searches and visualizes the results in their genomic context. Many data including protein sequences, protein network, interaction types for protein links, orthologous groups or full database dumps (license required) are downloadable.

Median values of the proportions of non-disease essential proteins among n (n ∈ {1, 2, 3, 4, 5, 6, 7}) neighbors of nonessential disease proteins (D−) and other proteins (O) in the protein interaction network constructed based on the STRING database.

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.

Supplementary Data 1. STRING protein-protein interaction network in lung. Supplementary Data 2. PROPER protein-protein interaction network in lung. Supplementary Data 3. STRING protein-protein interaction network in whole blood. Supplementary Data 4. PROPER protein-protein interaction network in whole blood. Supplementary Data 5. Genes causal for asthma in the lung GRN identified using Mendelian randomisation (Wald ratio method). Supplementary Data 6. Genes causal for asthma in the Blood GRN identified using Mendelian randomisation (Wald ratio method and inverse variance weighted). Supplementary Data 7. significantly enriched (hypergeometric test, FDR≤0.05 and 500 sets of Monte Carlo simulations) asthma-trait interactions. Supplementary Data 8. curated gene-disease associations from DisGeNet for the identified level 0-4 genes (hypergeometric test, FDR≤0.05). Supplementary Data 9. Comorbidity analysis using health records of 2051661 hospitalized patients, 26781 of which had asthma (ICD10-AM code J459). Supplementary Data 10. list of level 0-4 genes (hypergeometric test, FDR≤0.05 and 500 sets of Monte Carlo simulation) that are part of the druggable genome and/or have known drug targets and/or have been causally associated with asthma through Mendelian Randomization.

for enrichment in gene ontology biological processes. Glucose metabolism was found to be the most significant biological process, and is also highlighted in the STRING network map (Fig. 5).

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).

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...

OGBN-Proteins

Webpage: https://ogb.stanford.edu/docs/nodeprop/#ogbn-proteins

Usage in Python

import os.path as osp
import pandas as pd
import torch
import torch_geometric.transforms as T
from ogb.nodeproppred import PygNodePropPredDataset

class PygOgbnProteins(PygNodePropPredDataset):
  def _init_(self, meta_csv = None):
    root, name, transform = '/kaggle/input', 'ogbn-proteins', T.ToSparseTensor()
    if meta_csv is None:
      meta_csv = osp.join(root, name, 'ogbn-master.csv')
    master = pd.read_csv(meta_csv, index_col = 0)
    meta_dict = master[name]
    meta_dict['dir_path'] = osp.join(root, name)
    super()._init_(name = name, root = root, transform = transform, meta_dict = meta_dict)
  def get_idx_split(self, split_type = None):
    if split_type is None:
      split_type = self.meta_info['split']
    path = osp.join(self.root, 'split', split_type)
    if osp.isfile(os.path.join(path, 'split_dict.pt')):
      return torch.load(os.path.join(path, 'split_dict.pt'))
    if self.is_hetero:
      train_idx_dict, valid_idx_dict, test_idx_dict = read_nodesplitidx_split_hetero(path)
      for nodetype in train_idx_dict.keys():
        train_idx_dict[nodetype] = torch.from_numpy(train_idx_dict[nodetype]).to(torch.long)
        valid_idx_dict[nodetype] = torch.from_numpy(valid_idx_dict[nodetype]).to(torch.long)
        test_idx_dict[nodetype] = torch.from_numpy(test_idx_dict[nodetype]).to(torch.long)
        return {'train': train_idx_dict, 'valid': valid_idx_dict, 'test': test_idx_dict}
    else:
      train_idx = dt.fread(osp.join(path, 'train.csv'), header = None).to_numpy().T[0]
      train_idx = torch.from_numpy(train_idx).to(torch.long)
      valid_idx = dt.fread(osp.join(path, 'valid.csv'), header = None).to_numpy().T[0]
      valid_idx = torch.from_numpy(valid_idx).to(torch.long)
      test_idx = dt.fread(osp.join(path, 'test.csv'), header = None).to_numpy().T[0]
      test_idx = torch.from_numpy(test_idx).to(torch.long)
      return {'train': train_idx, 'valid': valid_idx, 'test': test_idx}

dataset = PygOgbnProteins()
split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']
graph = dataset[0] # PyG Graph object

Description

Graph: The ogbn-proteins dataset is an undirected, weighted, and typed (according to species) graph. Nodes represent proteins, and edges indicate different types of biologically meaningful associations between proteins, e.g., physical interactions, co-expression or homology [1,2]. All edges come with 8-dimensional features, where each dimension represents the strength of a single association type and takes values between 0 and 1 (the larger the value is, the stronger the association is). The proteins come from 8 species.

Prediction task: The task is to predict the presence of protein functions in a multi-label binary classification setup, where there are 112 kinds of labels to predict in total. The performance is measured by the average of ROC-AUC scores across the 112 tasks.

Dataset splitting: The authors split the protein nodes into training/validation/test sets according to the species which the proteins come from. This enables the evaluation of the generalization performance of the model across different species.

Note: For undirected graphs, the loaded graphs will have the doubled number of edges because the bidirectional edges will be added automatically.

Summary

Open Graph Benchmark

Website: https://ogb.stanford.edu

The Open Graph Benchmark (OGB) [3] is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner.

References

[1] Damian Szklarczyk, Annika L Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, Nadezhda T Doncheva, John H Morris, Peer Bork, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1):D607–D613, 2019. [2] Gene Ontology Consortium. The gene ontology resource: 20 years and still going strong. Nucleic Acids Research, 47(D1):D330–D338, 2018. [3] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchm...

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.

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

The proper development of the mammalian cerebral cortex requires precise protein synthesis and accurate regulation of protein expression levels. To reveal signatures of protein expression in developing mouse cortices, we here generate proteomic profiles of cortices at embryonic and postnatal stages using tandem mass spectrometry (MS/MS). We found that protein expression profiles are mostly consistent with biological features of the developing cortex. Gene Ontology (GO) and KEGG pathway analyses demonstrate conserved molecules that maintain cortical development such as proteins involved in metabolism. GO and KEGG pathway analyses further identify differentially expressed proteins that function at specific stages, for example proteins regulating the cell cycle in the embryonic cortex, and proteins controlling axon guidance in the postnatal cortex, suggesting that distinct protein expression profiles determine biological events in the developing cortex. Furthermore, the STRING network analysis has revealed that many proteins control a single biological event, such as the cell cycle regulation, through cohesive interactions, indicating a complex network regulation in the cortex. Our study has identified protein networks that control the cortical development and has provided a protein reference for further investigation of protein interactions in the cortex.