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56 results for “Biological Networks”
Dataset supporting the paper: Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks
<p>Dataset supporting the paper:</p> <p>Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. In Proceedings of CASC ’17, Beijing, China, September 18-22 2017, 15 pages. Springer, 2017.</p> <p>The files whose name starts with "SamplePoints" are text files containing the data that produced the plots in the paper.</p> <p>The files whose name starts with "Sys" show the Maple computations used to produce the data. The mw files are to be run with the Maple Computer Algebra System (https://www.maplesoft.com/products/maple/). Pdf printouts of these have also been included for those who do not have access to Maple.</p> <p> </p>
Data and code related to "Difficult control is related to instability in biologically inspired Boolean networks"
<p>This repository contains data and code related to the publication "Difficult control is related to instability in biologically inspired Boolean networks" by Bryan C. Daniels and Enrico Borriello.</p> <p>The python code in the `isolated_fixed_points_code` directory can be used to recreate all results in the paper. See the README.md file in the `isolated_fixed_points_code` directory for more information about how to run the code.</p> <p>The files `240916_cell_collective_ck_and_isolated_fp_data.csv`, `240916_iowa_database_ck_and_isolated_fp_data.csv`, and `240916_random_ck_and_isolated_fp_data.csv` contain data about the networks analyzed in the paper, including the number of attractors and mean control kernel size of each network.</p>
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph
<p>BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies.</p> <p>Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions.</p> <p>All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories.</p> <p>BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8.</p> <p>We provide BOCK 2.0 in three formats:</p> <ol> <li><strong>GraphML (Graph Markup Language)</strong>: a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).</li> <li><strong>XML (Extensible Markup Language)</strong>: a text-encoding system that is human-readable and compatible with many systems.</li> <li><strong>Neo4J import files</strong>: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.</li> </ol> <p> </p>
# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease
<p>Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.</p>
BioTAGME: A comprehensive platform for biological knowledge network analysis
<p><strong>A Knowledge Graph containing logical or physical relationships among biological elements</strong>. <br> This Network was built through BioTAGME, a system that combines TAGME, an entity-annotation framework based on Wikipedia corpus with a network-based inference methodology (i.e., DT-Hybrid).<br> <em>BioTAGME</em> exploits several Biological Ontologies as "ground truth" of significant bio-entities, such as <em>DisGeNET, DrugBank, STRING</em> and many more. <br> We Deployed <em>BioTAGME</em> on <em>PubMed</em>, where the aim was on extracting biological entities and their relations from titles and abstracts.<br> Biological entities are the <em>nodes</em> of our graph, while <em>edges</em> are the relations between them.<br> Edges are of three categories:</p> <ol> <li>Literature edges: interactions derived from publications.</li> <li> STRING: protein-protein associations stored in the STRING database.</li> <li>BioTAGME: interactions predicted by our tool.</li> </ol> <p> </p> <p>The <strong>network</strong> is released (<strong>BiotagmeNetwork.zip</strong>) in a neo4j compatible format. Such archive contains:</p> <ol> <li><strong>Edges.csv and Nodes.csv</strong> that contain the nodes and edges of our network, rispectively.</li> <li><strong>Name_Aliases.csv</strong>: contains the synonyms list of each biological entity.</li> <li><strong>Wiki_Titles.csv</strong>: contains the wikipedia pages title associated with the annotated entities.</li> <li><strong>wid1_wid2_pmid.csv</strong>: contains the associations between pairs of entities and pubmed id</li> <li><strong>BioIDs_WikiIDs.csv</strong>: contains the association between the biological entities annotated by BioTAGME, and the wikipedia pages that are associated. </li> </ol>
Dataset supporting the paper: "Identifying the Parametric Occurrence of Multiple Steady States for some Biological Networks"
<p>Dataset supporting the paper:</p> <p>Russell Bradford, James H. Davenport, Matthew England, Hassan Errami, Vladimir Gerdt, Dima Grigoriev, Charles Hoyt, Marek Košta, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber.<br> Identifying the Parametric Occurrence of Multiple Steady States for some Biological Networks.<br> To appear in the Journal of Symbolic Computation.</p> <p>We provide all the accompanying material for the main computations, split up into sections as presented in the paper. For Sections 3 and 9 the computations are introduced by a plain text ReadMe. For Sections 4 and 6 they are introduced by a Maple (also available ad pdf printout).</p>
main source codes and files of "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"
<p>These source codes and their related files are associated the study. "Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks"</p> <p>This study is in process of publication.</p>
Supporting data for "Reliable interpretability of biology-inspired deep neural networks"
<p><strong>Contents</strong></p> <p><em>data.tgz</em> contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder <em>data</em>. The archive contains the following subfolders:</p> <ul> <li><em>dtox</em><br> DTox results, one subfolder per seed <ul> <li><em>module_relevance.tsv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): compound identifier</li> <li>remaining columns: node identifiers (UniProt and Reactome IDs)</li> </ul> </li> <li><em>test_labels.csv</em>: predictions for the test set, with two columns: <ul> <li>truth: true label (0 or 1)</li> <li>predicted: predicted label (decimal number between 0 and 1)<br> </li> </ul> </li> </ul> </li> <li><em>mskimpact_[cancer type]_[experiment]</em><br> P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed<br> [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer)<br> [experiment] is one of original (original setup) and shuffled (shuffled labels)<br> </li> <li><em>pnet_[experiment]</em><br> P-NET results using the original (prostate cancer) dataset, one subfolder per seed<br> [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <ul> <li><em>node_importance.csv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): node name</li> <li>coef: original node importance scores</li> <li>coef_graph: indegree plus outdegree of node</li> <li>coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph > mean(coef_graph) + 5 sd(coef_graph) in the respective layer)</li> <li>coef_combined_zscore: scaled coef_combined</li> <li>coef_combined2: z(z(coef_graph) - z(coef))</li> <li>layer: layer of the node</li> </ul> </li> <li><em>predictions_test.csv</em>: predictions for the test set, with the following columns: <ul> <li>(first, unnamed): sample name</li> <li>pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0)</li> <li>pred_scores: probability of the predicted class</li> <li>y: true class (encoded as integer 1 or 0)</li> </ul> </li> <li><em>predictions_train.csv</em>: predictions for the training set (same columns as above)</li> <li><em>link_weights_[layer].csv</em>: only in subfolder 234_20080808; matrices with edge weights</li> </ul> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <p><em>v1.1.0 – 2023-06-28</em></p> <ul> <li>added DTox results</li> <li>added results of P-NET experiments with MSK-IMPACT 2017 dataset</li> </ul> <p><em>v1.0.0 – 2023-03-22</em></p> <ul> <li>initial release</li> </ul>
Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model
<p>Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of<br> the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs<br> can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a<br> promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features<br> learned by these models are still poorly understood and not easy to interpret.<br> We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an<br> easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which<br> biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It<br> combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors.<br> The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect<br> of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model<br> outperforms state-of-the-art visible machine learning models.</p>
Custom made python script using network assignment and scoring to estimate the impact of biological processes.
Open the record for dataset details and reuse information.
National Phenology Network tree phenology: Phenology, the timing of biological events such as bud break, plant flowering times and bird migration
Phenology is the study of recurring plant/animal phenophases. Environmental changes will likely impact phenological events at the species level and above. Most of the phenological changes are unknown at the species level and may have large impacts on natural ecosystems in the future. As part of a long term phenological experiment on forest ecosystems, since spring 2009 phenophases were observed on strategically selected forest species. This experiment is part of the National Phenology Network (USA-NPN).
Data from: Does biological intimacy shape ecological network structure? A test using a brood pollination mutualism on continental and oceanic islands
Biological intimacy—the degree of physical proximity or integration of partner taxa during their life cycles—is thought to promote the evolution of reciprocal specialization and modularity in the networks formed by co‐occurring mutualistic species, but this hypothesis has rarely been tested. Here, we test this "biological intimacy hypothesis" by comparing the network architecture of brood pollination mutualisms, in which specialized insects are simultaneously parasites (as larvae) and pollinators (as adults) of their host plants to that of other mutualisms which vary in their biological intimacy (including ant‐myrmecophyte, ant‐extrafloral nectary, plant‐pollinator and plant‐seed disperser assemblages). We use a novel dataset sampled from leafflower trees (Phyllanthaceae: Phyllanthus s. l. [Glochidion]) and their pollinating leafflower moths (Lepidoptera: Epicephala) on three oceanic islands (French Polynesia) and compare it to equivalent published data from congeners on continental islands (Japan). We infer taxonomic diversity of leafflower moths using multilocus molecular phylogenetic analysis and examine several network structural properties: modularity (compartmentalization), reciprocality (symmetry) of specialization and algebraic connectivity. We find that most leafflower‐moth networks are reciprocally specialized and modular, as hypothesized. However, we also find that two oceanic island networks differ in their modularity and reciprocal specialization from the others, as a result of a supergeneralist moth taxon which interacts with nine of 10 available hosts. Our results generally support the biological intimacy hypothesis, finding that leafflower‐moth networks (usually) share a reciprocally specialized and modular structure with other intimate mutualisms such as ant‐myrmecophyte symbioses, but unlike nonintimate mutualisms such as seed dispersal and nonintimate pollination. Additionally, we show that generalists—common in nonintimate mutualisms—can also evolve in intimate mutualisms, and that their effect is similar in both types of assemblages: once generalists emerge they reshape the network organization by connecting otherwise isolated modules.
Mechanistic interactions as the origin of modularity in biological networks
<p>Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements - such as the species in an ecosystem - as finite-state machines (FSMs) and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity likely emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.</p>
An innovative approach combining metabarcoding and ecological interaction networks for selecting candidate biological control agents
<p>Classical biological control (CBC) can be used to decrease the density of invasive species to below an acceptable ecological and economic threshold. Natural enemies specific to the invasive species are selected from its native range and released into the invaded range. This approach has drawbacks, despite the performance of specificity tests to ensure its safety, because the fundamental host range defined under controlled conditions does not represent the actual host range <em>in natura, </em>and these tests omit indirect interactions within community.</p> <p>We focus on <em>Sonchus oleraceus</em> (Asteraceae), a weed species originating from Western Palearctic that is invasive in many countries and notably in Australia. We explore how analyses of interaction network within its native range can be used to 1) inventory herbivores associated to the target plant, 2) characterize their ecological host ranges, and 3) guide the selection of candidate biocontrol agents considering interactions with species from higher trophic levels. Arthropods were collected from plant community sympatric to <em>S. oleraceus</em>, in three bioclimatic regions, and interactions were inferred by a combination of molecular and morphological approaches.</p> <p>The networks reconstructed were structured in several trophic levels from basal species (plant community), to intermediate and top species (herbivorous arthropods and their natural enemies). The subnetwork centered on <em>S. oleraceus</em> related interactions contained 116 taxa and 213 interactions. We identified 47 herbivores feeding on <em>S. oleraceus</em>, 15 of which were specific to the target species. Some discrepancies with respect to published findings or conventional specificity tests suggested possible insufficient sampling effort for the recording of interactions or the existence of cryptic species. Among potential candidate agents, 6 exhibited interactions with natural enemies.</p> <p>Synthesis and applications: Adopting a network approach as prerequisite step of the classical biological control program can provide a rapid screening of potential agents to be tested in priority. Once ecological host range defined, we suggest that priority should be given to agent used by a minimum species, and, when they exist, to agents that possess enemies from the most distant taxonomical group from those occurring in the range of introduction.</p>
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph 2.0
<p>BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies.</p> <p>Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions.</p> <p>All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories.</p> <p>BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8.</p> <p>We provide BOCK 2.0 in three formats:</p> <ol> <li><strong>GraphML (Graph Markup Language)</strong>: a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).</li> <li><strong>XML (Extensible Markup</strong> <strong>Language)</strong>: a text-encoding system that is human-readable and compatible with many systems.</li> <li><strong>Neo4J import files</strong>: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.</li> </ol>
Network analysis reveals rare disease signatures across multiple levels of biological organization - Co-expression dataset
<p>The GTEx-derived co-expression data in 38 tissues generated in Buphamalai et.al., Network analysis reveals rare disease signatures across multiple levels of biological organization, Nature Communications 2021. Please see the publication's Methods section for details.</p>
Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial & nuclear biology
<p>This online depository corresponds to manuscript : <em>Protein network analysis links the NSL complex to Parkinson’s disease via mitochondrial & nuclear biology.</em></p> <p><strong>Authors: </strong><em>Katie Kelly, Patrick A. Lewis, Helene Plun-Favreau, Claudia Manzoni</em></p> <p>Whilst the majority (~90-95%) of PD cases are sporadic, much of our understanding of the pathophysiological basis of disease can be traced back to the study of rare, monogenic forms of disease. However, in the past decade, the availability of Genome-Wide Association Studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring an increased risk of developing PD across the population. </p> <p>A recently developed mitophagy screening assay of GWAS candidates, has functionally implicated the non-specific lethal (NSL) complex, a chromatin remodeler, in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the interactome of the NSL complex, to unpick its relevance to PD progression. The mitochondrial interactome of the NSL complex has been built, mining 3 separate repositories: PINOT, HIPPIE and MIST, for curated, literature-derived protein-protein interaction (PPI) data. A multi-layered approach has been taken to; i) build the ‘mitochondrial’ NSL interactome, applying PD gene-set enrichment analysis to explore the relevance of the NSL mitochondrial interactome to PD and, ii) build the PD-oriented NSL interactome, using functional enrichment, to uncover biological pathways underpinning the NSL /PD association.</p>
Data from: Does biological intimacy shape ecological network structure? A test using a brood pollination mutualism on continental and oceanic islands
Open the record for dataset details and reuse information.
An innovative approach combining metabarcoding and ecological interaction networks for selecting candidate biological control agents
Open the record for dataset details and reuse information.
Mechanistic interactions as the origin of modularity in biological networks
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.