199:. Such algorithms are typically based on linearity, independence or normality assumptions, which must be verified on a case-by-case basis. Clustering or some form of statistical classification is typically employed to perform an initial organization of the high-throughput mRNA expression values derived from microarray experiments, in particular to select sets of genes as candidates for network nodes. The question then arises: how can the clustering or classification results be connected to the underlying biology? Such results can be useful for pattern classification – for example, to classify subtypes of
252:, ubiquitylation, methylation, etc.). Primary input into the inference algorithm would be data from a set of experiments measuring protein activation / inactivation (e.g., phosphorylation / dephosphorylation) across a set of proteins. Inference for such signalling networks is complicated by the fact that total concentrations of signalling proteins will fluctuate over time due to transcriptional and translational regulation. Such variation can lead to statistical
170:. Briefly, methods using high-throughput data for inference of regulatory networks rely on searching for patterns of partial correlation or conditional probabilities that indicate causal influence. Such patterns of partial correlations found in the high-throughput data, possibly combined with other supplemental data on the genes or proteins in the proposed networks, or combined with other information on the organism, form the basis upon which such
424:
20:
602:
group there is a mathematical interpretation that assumes that features that persist for a wide range of parameters are "true" features and features persisting for only a narrow range of parameters are noise, although the theoretical justification for this is unclear. This technique has been used for
493:
method for assessing the reliability of protein-protein interaction data is based on the use of standards. MIscore gives an estimation of confidence weighting on all available evidence for an interacting pair of proteins. The method allows weighting of evidence provided by different sources, provided
488:
Network confidence is a way to measure how sure one can be that the network represents a real biological interaction. We can do this via contextual biological information, counting the number of times an interaction is reported in the literature, or group different strategies into a single score. the
700:
Gene annotation databases are commonly used to evaluate the functional properties of experimentally derived gene sets. Annotation
Enrichment Analysis (AEA) is used to overcome biases from overlap statistical methods used to assess these associations. It does this by using gene/protein annotations to
479:
of a network is a measure of the tendency of the nodes to cluster together. High transitivity means that the network contains communities or groups of nodes that are densely connected internally. In biological networks, finding these communities is very important, because they can reflect functional
458:
The initial data used to make the inference can have a huge impact on the accuracy of the final inference. Network data is inherently noisy and incomplete sometimes due to evidence from multiple sources that don't overlap or contradictory data. Data can be sourced in multiple ways to include manual
573:
Centrality gives an estimation on how important a node or edge is for the connectivity or the information flow of the network. It is a useful parameter in signalling networks and it is often used when trying to find drug targets. It is most commonly used in PINs to determine important proteins and
564:
A motif is defined as a frequent and unique sub-graph. By counting all the possible instances, listing all patterns, and testing isomorphisms we can derive crucial information about a network. They're suggested to be the basic building blocks complex biological networks. The computational research
508:
Closeness, a.k.a. closeness centrality, is a measure of centrality in a network and is calculated as the reciprocal of the sum of the length of the shortest paths between the node and all other nodes in the graph. This measure can be used to make inferences in all graph types and analysis methods.
48:
as well as a quantitative framework for developmental processes. Good network inference requires proper planning and execution of an experiment, thereby ensuring quality data acquisition. Optimal experimental design in principle refers to the use of statistical and or mathematical concepts to plan
321:
A neuronal network is composed to represent neurons with each node and synapses for the edges, which are typically weighted and directed. the weights of edges are usually adjusted by the activation of connected nodes. The network is usually organized into input layers, hidden layers, and output
190:
or protein molecule that functions as a transcriptional activator or inhibitor of the target gene. If the gene is an activator, then it is the source of a positive regulatory connection; if an inhibitor, then it is the source of a negative regulatory connection. Computational algorithms take as
138:
A network is a set of nodes and a set of directed or undirected edges between the nodes. Many types of biological networks exist, including transcriptional, signalling and metabolic. Few such networks are known in anything approaching their complete structure, even in the simplest
353:
we can discover and understand how these interactions link together within the system's network. It also allows us to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level.
574:
their functions. Centrality can be measured in different ways depending on the graph and the question that needs answering, they include the degree of nodes or the number of connected edges to a node, global centrality measures, or via random walks which is used by the
598:(TDA) provides a general framework to analyze high dimensional, incomplete, and noisy data in a way that reduces dimensional and gives a robustness to noise. The idea that is that the shape of data sets contains relevant information. When this information is a
207:(pharmacogenomics). But to understand the relationships between the genes, that is, to more precisely define the influence of each gene on the others, the scientist typically attempts to reconstruct the transcriptional regulatory network.
565:
has focused on improving existing motif detection tools to assist the biological investigations and allow larger networks to be analyzed. Several different algorithms have been provided so far, which are elaborated in the next section.
96:
approaches. it can also be done by the application of a correlation-based inference algorithm, as will be discussed below, an approach which is having increased success as the size of the available microarray sets keeps increasing
490:
480:
modules and protein complexes The uncertainty about the connectivity may distort the results and should be taken into account when the transitivity and other topological descriptors are computed for inferred networks.
295:(PINs) visualize the physical relationships between proteins inside a cell. in a PIN, proteins are the nodes and their interactions are the undirected edges. PINs can be discovered with a variety of methods including;
1877:
336:
A food web is an interconnected directional graph of what eats what in an ecosystem. The members of the ecosystem are the nodes and if a member eats another member then there is a directed edge between those 2 nodes.
49:
for data acquisition. This must be done in such a way that the data information content is enriched, and a sufficient amount of data is collected with enough technical and biological replicates where necessary.
555:
search, centrality analysis, topological clustering, and shortest paths. These are but a few examples, each of these techniques use the general idea of focusing on the topology of a network to make inferences.
247:
Signal transduction networks use proteins for the nodes and directed edges to represent interaction in which the biochemical conformation of the child is modified by the action of the parent (e.g. mediated by
550:
Topology
Analysis analyzes the topology of a network to identify relevant participates and substructures that may be of biological significance. The term encompasses an entire class of techniques such as
143:. Still less is known on the parameters governing the behavior of such networks over time, how the networks at different levels in a cell interact, and how to predict the complete state description of a
523:
Betweeness, a.k.a. betweenness centrality, is a measure of centrality in a graph based on shortest paths. The betweenness for each node is the number of these shortest paths that pass through the node.
410:
and complexes of these. Gene regulatory networks can be modeled in numerous ways including; Coupled ordinary differential equations, Boolean networks, Continuous networks, and
Stochastic gene networks.
578:
algorithm to assign weight to each webpage. The centrality measures may be affected by errors due to noise on measurement and other causes. Therefore, the topological descriptors should be defined as
37:. By using these networks to analyze patterns in biological systems, such as food-webs, we can visualize the nature and strength of these interactions between species, DNA, proteins, and more.
2042:
Schmidt S, Post TM, Boroujerdi MA, van
Kesteren C, Ploeger BA, Pasqua OE, Danhof M (2011). "Disease Progression Analysis: Towards Mechanism-Based Models". In Kimko HH, Peck CC (eds.).
603:
progression analysis of disease, viral evolution, propagation of contagions on networks, bacteria classification using molecular spectroscopy, and much more in and outside of biology.
467:
A network's diameter is the maximum number of steps separating any two nodes and can be used to determine the How connected a graph is, in topology analysis, and clustering analysis.
459:
curation of scientific literature put into databases, High-throughput datasets, computational predictions, and text mining of old scholarly articles from before the digital era.
127:
Cross-check how well the results meet the expectations. The process is terminated upon obtaining a good model fit to data, otherwise, there is need for model re-adjustment.
627:
of its constituent edges is minimized. This method can be used to determine the network diameter or redundancy in a network. there are many algorithms for this including
494:
the data is represented following the standards created by the IMEx consortium. The weights are number of publications, detection method, interaction evidence type.
2618:
Hoffmann R, Krallinger M, Andres E, Tamames J, Blaschke C, Valencia A (May 2005). "Text mining for metabolic pathways, signaling cascades, and protein networks".
2260:
Offroy M, Duponchel L (March 2016). "Topological data analysis: A promising big data exploration tool in biology, analytical chemistry and physical chemistry".
1778:"Analysis of dermal papilla cell interactome using STRING database to profile the ex vivo hair growth inhibition effect of a vinca alkaloid drug, colchicine"
277:
and regulatory interactions that guide these reactions. Primary input into an algorithm would be data from a set of experiments measuring metabolite levels.
1940:
Cantwell GT, Liu Y, Maier BF, Schwarze AC, Serván CA, Snyder J, St-Onge G (June 2020). "Thresholding normally distributed data creates complex networks".
345:
These networks are defined by a set of pairwise interactions between and within a species that is used to understand the structure and function of larger
650:
groups objects (nodes) such that objects in the same cluster are more similar to each other than to those in other clusters. This can be used to perform
542:. By measuring the attributes in the previous section we can utilize many different techniques to create accurate inferences based on biological data.
761:
398:
A gene regulatory network is a set of molecular regulators that interact with each other and with other substances in the cell. The regulator can be
2059:
1517:
256:. Accordingly, more sophisticated statistical techniques must be applied to analyse such datasets.(very important in the biology of cancer)
681:
882:
Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM (June 2020). "Gene regulatory network inference resources: A practical overview".
186:
Genes are the nodes and the edges are directed. A gene serves as the source of a direct regulatory edge to a target gene by producing an
1338:
158:. This article focuses on inference of biological network structure using the growing sets of high-throughput expression data for
1317:
Tieri P, Farina L, Petti M, Astolfi L, Paci P, Castiglione F (2018). "Network
Inference and Reconstruction in Bioinformatics".
2077:"Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival"
791:
368:
DNA-DNA chromatin networks are used to clarify the activation or suppression of genes via the relative location of strands of
624:
73:
1202:"Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles"
384:
is located. Network analysis can provide vital support in understanding relationships among different areas of the genome.
636:
222:
155:
1829:"Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study"
1256:
Raimondo S, De
Domenico M (February 2021). "Measuring topological descriptors of complex networks under uncertainty".
976:"Delineation of key regulatory elements identifies points of vulnerability in the mitogen-activated signaling network"
195:
expression levels of the genes under consideration for inclusion in the network, returning an estimate of the network
1727:"Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks"
632:
216:
595:
1442:"Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection"
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814:
583:
2046:. AAPS Advances in the Pharmaceutical Sciences Series. Vol. 1. New York, NY: Springer. pp. 433–455.
810:
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work. Such algorithms can be of use in inferring the topology of any network where the change in state of one
2305:"Annotation enrichment analysis: an alternative method for evaluating the functional properties of gene sets"
2666:
685:
628:
393:
350:
1500:
Isono E, Schwechheimer C (2010). "Co-immunoprecipitation and
Protein Blots". In Hennig L, Köhler C (eds.).
599:
518:
476:
304:
85:
44:. Recent examples of application of network theory in biology include applications to understanding the
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612:
40:
The analysis of biological networks with respect to diseases has led to the development of the field of
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1959:
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Villaveces JM, Jiménez RC, Porras P, Del-Toro N, Duesbury M, Dumousseau M, et al. (2015-01-01).
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1983:
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34:
1109:"Computational inference of gene regulatory networks: Approaches, limitations and opportunities"
1200:
Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al. (January 2007).
701:
infer which annotations are over-represented in a list of genes/proteins taken from a network.
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2527:
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2014:
1967:
1906:
1897:
Brin S, Page L (1998-04-01). "The anatomy of a large-scale hypertextual Web search engine".
1848:
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89:
41:
2195:"Topological data analysis of contagion maps for examining spreading processes on networks"
2193:
Taylor D, Klimm F, Harrington HA, Kramár M, Mischaikow K, Porter MA, Mucha PJ (July 2015).
1504:. Methods in Molecular Biology. Vol. 655. Totowa, NJ: Humana Press. pp. 377–387.
1151:
Marbach D, Costello JC, KĂĽffner R, Vega NM, Prill RJ, Camacho DM, et al. (July 2012).
1072:
van
Someren EP, Wessels LF, Backer E, Reinders MJ (July 2002). "Genetic network modeling".
579:
249:
230:
148:
77:
2455:"Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design"
111:
Ensure that high quality data is collected with all the required variables being measured
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2320:
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1963:
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19:
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1177:
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1049:
1024:
1000:
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951:
926:
655:
533:
381:
316:
1910:
692:, Distribution-based clustering, Density-based clustering, and Grid-based clustering.
423:
2660:
2545:
Ritz A, Poirel CL, Tegge AN, Sharp N, Simmons K, Powell A, et al. (2016-03-03).
1602:
1585:
1303:
974:
Jailkhani N, Ravichandran S, Hegde SR, Siddiqui Z, Mande SC, Rao KV (December 2011).
911:
666:
552:
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1987:
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1441:
1348:
795:
372:. These interactions can be understood by analyzing commonalities amongst different
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1662:
539:
2019:
64:
Involves a thorough literature and database search or seeking an expert's opinion.
2051:
1509:
1218:
1125:
1108:
895:
1971:
1287:
286:
253:
2478:
2140:
Proceedings of the
National Academy of Sciences of the United States of America
2081:
Proceedings of the
National Academy of Sciences of the United States of America
1481:
Oates CJ, Mukherjee S (2012). "Structural inference using nonlinear dynamics".
2281:
1844:
663:
377:
273:
networks use nodes to represent chemical reactions and directed edges for the
270:
167:
45:
2631:
2028:
1918:
1743:
1085:
680:, Sequence analysis, antimicrobial activity analysis, and many other fields.
2160:
2101:
1646:
1367:
1364:
Causation, Prediction, and Search: Adaptive Computation and Machine Learning
852:
369:
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30:
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1979:
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1611:
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1237:
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1093:
1058:
1040:
1009:
960:
903:
757:
2562:
2547:"Pathways on demand: automated reconstruction of human signaling networks"
2523:
1554:
991:
229:, and a pair of nodes is connected with an edge if there is a significant
2453:
Li J, Zhou D, Qiu W, Shi Y, Yang JJ, Chen S, et al. (January 2018).
1794:
575:
331:
196:
140:
2430:
2413:
2228:
1408:
1168:
677:
407:
163:
2328:
1901:. Proceedings of the Seventh International World Wide Web Conference.
1586:"Biochemical approaches for discovering protein-protein interactions"
674:
200:
1878:"Centrality analysis | Network analysis of protein interaction data"
942:
1954:
1270:
2211:
1399:
670:
18:
119:
This process is mathematical rigorous and computationally costly.
57:
The general cycle to modeling biological networks is as follows:
1541:
Wittig I, Braun HP, Schägger H (June 2006). "Blue native PAGE".
1440:
Guthke R, Möller U, Hoffmann M, Thies F, Töpfer S (April 2005).
1113:
Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms
1025:"Network-based approaches to quantify multicellular development"
884:
Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms
799:
226:
204:
192:
159:
2512:
Sheng Wu Gong Cheng Xue Bao = Chinese Journal of Biotechnology
418:
403:
399:
187:
927:"Network medicine: a network-based approach to human disease"
1776:
Hsia CW, Ho MY, Shui HA, Tsai CB, Tseng MJ (February 2015).
2594:
615:
is a common problem in graph theory that tries to find the
147:
cell or bacterial organism at a given point in the future.
1625:
Bascompte J (July 2009). "Disentangling the web of life".
780:
769:
538:
For our purposes, network analysis is closely related to
1319:
Encyclopedia of Bioinformatics and Computational Biology
821:
Within Species and Between Species Interaction Networks
1023:
Jackson MD, Duran-Nebreda S, Bassel GW (October 2017).
434:
341:
Within species and between species interaction networks
291:
One of the most intensely studied networks in biology
1678:"Social networks in the guppy (Poecilia reticulata)"
1153:"Wisdom of crowds for robust gene network inference"
925:
Barabási AL, Gulbahce N, Loscalzo J (January 2011).
154:
There is great interest in network medicine for the
2044:
Clinical Trial Simulations: Applications and Trends
2414:"Constructing transcriptional regulatory networks"
2362:"FANMOD: a tool for fast network motif detection"
1725:Emmert-Streib F, Dehmer M, Haibe-Kains B (2014).
2134:Chan JM, Carlsson G, Rabadan R (November 2013).
2075:Nicolau M, Levine AJ, Carlsson G (April 2011).
623:(or nodes) in a graph such that the sum of the
2510:Liu W, Li L, Ye H, Tu W (November 2017). "".
2007:Bulletin of the American Mathematical Society
1676:Croft DP, Krause J, James R (December 2004).
1146:
1144:
307:, blue native gel electrophoresis, and more.
72:A formalism to model your system, usually an
8:
203:, or to predict differential responses to a
1782:International Journal of Molecular Sciences
1731:Frontiers in Cell and Developmental Biology
1383:"Network Inference and Biological Dynamics"
669:, and so much more. It has applications in
151:, in this sense, is still in its infancy .
1251:
1249:
1247:
2570:
2486:
2429:
2377:
2336:
2236:
2210:
2169:
2159:
2110:
2100:
2018:
1953:
1852:
1803:
1793:
1752:
1742:
1701:
1601:
1457:
1416:
1398:
1362:Sprites P, Glamour C, Scheines R (2000).
1269:
1227:
1217:
1176:
1124:
1048:
999:
950:
586:encoding the uncertainty on their value.
1381:Oates CJ, Mukherjee S (September 2012).
708:
1584:Miernyk JA, Thelen JJ (February 2008).
1029:Journal of the Royal Society, Interface
874:
726:FANMOD, ChIP-on-chip, position–weight
293:, Protein-protein interaction networks
2407:
2405:
2403:
2401:
2399:
2397:
776:Protein-Protein Interaction Networks
178:can affect the state of other nodes.
7:
2551:npj Systems Biology and Applications
723:Transcriptional regulatory networks
281:Protein-protein interaction networks
2303:Glass K, Girvan M (February 2014).
684:come in many forms as well such as
225:, where each node corresponds to a
221:A gene co-expression network is an
191:primary input data measurements of
182:Transcriptional regulatory networks
2412:Blais A, Dynlacht BD (July 2005).
1899:Computer Networks and ISDN Systems
1331:10.1016/B978-0-12-809633-8.20290-2
14:
2360:Wernicke S, Rasche F (May 2006).
1682:Proceedings. Biological Sciences
1603:10.1111/j.1365-313X.2007.03316.x
1387:The Annals of Applied Statistics
1107:Banf M, Rhee SY (January 2017).
422:
696:Annotation enrichment analysis
74:ordinary differential equation
1:
2379:10.1093/bioinformatics/btl038
2136:"Topology of viral evolution"
2020:10.1090/S0273-0979-09-01249-X
1911:10.1016/S0169-7552(98)00110-X
1459:10.1093/bioinformatics/bti226
779:FANMOD, NETBOX, Text Mining,
740:FANMOD, Paired Design, WGCNA
2052:10.1007/978-1-4419-7415-0_19
1510:10.1007/978-1-60761-765-5_25
1219:10.1371/journal.pbio.0050008
1126:10.1016/j.bbagrm.2016.09.003
896:10.1016/j.bbagrm.2019.194430
737:Gene Co-Expression Networks
728:matrices, AlignACE, MDScan,
156:modelling biological systems
27:Biological network inference
1972:10.1103/PhysRevE.101.062302
1502:Plant Developmental Biology
1288:10.1103/PhysRevE.103.022311
829:DNA-DNA Chromatin Networks
682:Cluster analysis algorithms
380:where a particular gene or
233:relationship between them.
211:Gene co-expression networks
2693:
2479:10.1038/s41598-017-18705-z
594:Topological Clustering or
531:
516:
501:
391:
361:
358:DNA-DNA chromatin networks
329:
314:
284:
263:
240:
217:Gene Co-Expression Network
214:
2282:10.1016/j.aca.2015.12.037
837:Gene Regulatory Networks
596:Topological Data Analysis
29:is the process of making
2632:10.1126/stke.2832005pe21
1744:10.3389/fcell.2014.00038
1086:10.1517/14622416.3.4.507
931:Nature Reviews. Genetics
637:Floyd–Warshall algorithm
584:probability distribution
528:Network analysis methods
388:Gene regulatory networks
376:, a fixed position on a
2418:Genes & Development
2161:10.1073/pnas.1313480110
2102:10.1073/pnas.1102826108
1845:10.1093/database/bau131
1647:10.1126/science.1170749
710:Network Analysis Tools
686:Hierarchical clustering
394:Gene regulatory network
2262:Analytica Chimica Acta
1694:10.1098/rsbl.2004.0206
1688:(Suppl 6): S516–S519.
1041:10.1098/rsif.2017.0484
705:Network analysis tools
633:Bellman–Ford algorithm
590:Topological Clustering
477:clustering coefficient
431:This section is empty.
305:co-immunoprecipitation
102:Hypothesis/assumptions
86:Least-angle regression
33:and predictions about
23:
2563:10.1038/npjsba.2016.2
2524:10.13345/j.cjb.170006
2199:Nature Communications
1555:10.1038/nprot.2006.62
992:10.1101/gr.116145.110
660:information retrieval
613:shortest path problem
22:
1795:10.3390/ijms16023579
859:Bayesian probability
745:Signal transduction
639:just to name a few.
629:Dijkstra's algorithm
582:with the associated
560:Network Motif Search
475:The transitivity or
297:Two-hybrid Screening
2471:2018NatSR...8..622L
2431:10.1101/gad.1325605
2321:2014NatSR...4E4191G
2274:2016AcAC..910....1O
2221:2015NatCo...6.7723T
2152:2013PNAS..11018566C
2146:(46): 18566–18571.
2093:2011PNAS..108.7265N
2003:"Topology and data"
2001:Carlsson G (2009).
1964:2020PhRvE.101f2302C
1639:2009Sci...325..416B
1483:CRiSM Working Paper
1280:2021PhRvE.103b2311R
748:FANMOD, PathLinker
711:
652:pattern recognition
643:Clustering analysis
569:Centrality Analysis
347:ecological networks
243:Signal transduction
237:Signal transduction
134:Biological networks
105:Experimental design
35:biological networks
2459:Scientific Reports
2309:Scientific Reports
2229:10.1038/ncomms8723
1409:10.1214/11-AOAS532
1169:10.1038/nmeth.2016
753:Metabolic Network
709:
690:k-means clustering
484:Network confidence
415:Network attributes
275:metabolic pathways
116:Network inference
94:Information theory
24:
2518:(11): 1791–1801.
2424:(13): 1499–1511.
2329:10.1038/srep04191
2087:(17): 7265–7270.
2061:978-1-4419-7415-0
1942:Physical Review E
1633:(5939): 416–419.
1590:The Plant Journal
1519:978-1-60761-765-5
1258:Physical Review E
1035:(135): 20170484.
986:(12): 2067–2081.
844:
843:
787:Neuronal Network
546:Topology analysis
451:
450:
266:Metabolic Network
260:Metabolic network
124:Model refinement
108:Data acquisition
82:Linear regression
16:Type of inference
2684:
2652:
2651:
2615:
2609:
2608:
2606:
2605:
2591:
2585:
2584:
2574:
2542:
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2372:(9): 1152–1153.
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2293:
2257:
2251:
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2163:
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1992:
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1884:
1873:
1867:
1866:
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1788:(2): 3579–3598.
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1543:Nature Protocols
1538:
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1531:
1497:
1491:
1490:
1478:
1472:
1471:
1461:
1452:(8): 1626–1634.
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1430:
1420:
1402:
1393:(3): 1209–1235.
1378:
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1371:
1366:(2nd ed.).
1359:
1353:
1352:
1314:
1308:
1307:
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1074:Pharmacogenomics
1069:
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1020:
1014:
1013:
1003:
971:
965:
964:
954:
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915:
879:
864:Network medicine
712:
648:Cluster analysis
463:Network diameter
446:
443:
433:You can help by
426:
419:
351:network analysis
311:Neuronal network
223:undirected graph
90:Bayesian network
69:Model selection
61:Prior knowledge
42:network medicine
2692:
2691:
2687:
2686:
2685:
2683:
2682:
2681:
2672:Systems biology
2657:
2656:
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2603:
2601:
2593:
2592:
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1948:(6–1): 062302.
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1880:
1875:
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1775:
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1264:(2–1): 022311.
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1106:
1105:
1101:
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980:Genome Research
973:
972:
968:
943:10.1038/nrg2918
924:
923:
919:
881:
880:
876:
872:
849:
824:FANMOD, NETBOX
792:Neural Designer
718:Analysis Tools
707:
698:
645:
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580:random variable
576:Google PageRank
571:
562:
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250:phosphorylation
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149:Systems biology
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78:boolean network
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2620:Science's STKE
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2366:Bioinformatics
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2013:(2): 255–308.
1993:
1932:
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1889:
1868:
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1717:
1668:
1617:
1596:(4): 597–609.
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1549:(1): 418–428.
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1446:Bioinformatics
1432:
1373:
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1163:(8): 796–804.
1157:Nature Methods
1140:
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656:image analysis
644:
641:
608:
607:Shortest paths
605:
591:
588:
570:
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561:
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544:
534:Network Theory
532:Main article:
529:
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517:Main article:
514:
511:
502:Main article:
499:
496:
485:
482:
472:
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452:
449:
448:
442:September 2022
429:
427:
416:
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392:Main article:
389:
386:
382:genetic marker
362:Main article:
359:
356:
342:
339:
330:Main article:
327:
324:
317:Neural Network
315:Main article:
312:
309:
285:Main article:
282:
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264:Main article:
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241:Main article:
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2012:
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1965:
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667:data analysis
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59:
58:
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2619:
2613:
2602:. Retrieved
2598:
2589:
2557:(1): 16002.
2554:
2550:
2540:
2515:
2511:
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2084:
2080:
2070:
2043:
2037:
2010:
2006:
1996:
1945:
1941:
1935:
1902:
1898:
1892:
1881:. Retrieved
1871:
1836:
1832:
1822:
1785:
1781:
1771:
1734:
1730:
1720:
1685:
1681:
1671:
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1206:PLOS Biology
1205:
1195:
1160:
1156:
1119:(1): 41–52.
1116:
1112:
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1067:
1032:
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1018:
983:
979:
969:
937:(1): 56–68.
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883:
877:
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727:
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646:
619:between two
610:
593:
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540:graph theory
537:
522:
507:
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474:
471:Transitivity
466:
457:
454:Data sources
439:
435:adding to it
430:
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320:
300:
292:
290:
269:
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92:or based on
56:
39:
26:
25:
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2205:(1): 7723.
1325:: 805–813.
664:statistical
519:Betweenness
513:Betweenness
349:. By using
287:Interactome
254:confounding
168:metabolites
2661:Categories
2604:2022-05-05
2465:(1): 622.
1955:1902.08278
1883:2022-05-05
1876:EMBL-EBI.
1839:: bau131.
1271:2009.06326
870:References
806:Food Webs
635:, and the
378:chromosome
271:Metabolite
172:algorithms
145:eukaryotic
46:cell cycle
31:inferences
2677:Inference
2599:bio.tools
2212:1408.1168
2029:0273-0979
1919:0169-7552
1400:1112.1047
1368:MIT Press
1304:221655165
1212:(1): e8.
912:207895066
853:Cytoscape
770:ModelSEED
756:FANMOD,
504:Closeness
498:Closeness
370:chromatin
364:Chromatin
326:Food webs
2648:15301069
2640:15886388
2595:"NetBox"
2581:28725467
2532:29202516
2497:29330528
2440:15998805
2388:16455747
2347:24569707
2290:26873463
2268:: 1–11.
2247:26194875
2180:24170857
2121:21482760
1988:67856476
1980:32688475
1863:25652942
1833:Database
1814:25664862
1763:25364745
1712:15801620
1655:19628856
1612:18269571
1571:19715017
1563:17406264
1528:20734274
1468:15613398
1427:23284600
1349:65155962
1296:33735966
1238:17214507
1187:22796662
1135:27641093
1094:12164774
1059:29021161
1010:21865350
961:21164525
904:31678629
847:See also
840:FANMOD,
832:FANMOD,
809:FANMOD,
790:FANMOD,
715:Network
621:vertices
600:homology
332:Food Web
322:layers.
301:in vitro
197:topology
164:proteins
141:bacteria
2572:5516854
2488:5766625
2467:Bibcode
2338:3935204
2317:Bibcode
2270:Bibcode
2238:4566922
2217:Bibcode
2171:3831954
2148:Bibcode
2112:3084136
2089:Bibcode
1960:Bibcode
1927:7587743
1854:4316181
1805:4346914
1754:4207011
1703:1810091
1663:2249052
1635:Bibcode
1627:Science
1418:3533376
1276:Bibcode
1229:1764438
1178:3512113
1050:5665831
1001:3227097
952:3140052
800:Darknet
796:Neuroph
678:ecology
625:weights
491:MIscore
408:protein
2646:
2638:
2579:
2569:
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2345:
2335:
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2178:
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2109:
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1986:
1978:
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1737:: 38.
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