-
1
-
-
84861980130
-
Interactions between the microbiota and the immune system
-
1 Hooper, L.V., et al. Interactions between the microbiota and the immune system. Science 336 (2012), 1268–1273.
-
(2012)
Science
, vol.336
, pp. 1268-1273
-
-
Hooper, L.V.1
-
2
-
-
84978115999
-
The microbiome and innate immunity
-
2 Thaiss, C.A., et al. The microbiome and innate immunity. Nature 535 (2016), 65–74.
-
(2016)
Nature
, vol.535
, pp. 65-74
-
-
Thaiss, C.A.1
-
3
-
-
84929298149
-
Microbiota and host nutrition across plant and animal kingdoms
-
3 Hacquard, S., et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17 (2015), 603–616.
-
(2015)
Cell Host Microbe
, vol.17
, pp. 603-616
-
-
Hacquard, S.1
-
4
-
-
84912109726
-
Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition
-
4 Pop, M., et al. Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition. Genome Biol., 15, 2014, R76.
-
(2014)
Genome Biol.
, vol.15
, pp. R76
-
-
Pop, M.1
-
5
-
-
84945303447
-
The gut microbiota and Type 1 Diabetes
-
5 Gülden, E., et al. The gut microbiota and Type 1 Diabetes. Clin. Immunol. 159 (2015), 143–153.
-
(2015)
Clin. Immunol.
, vol.159
, pp. 143-153
-
-
Gülden, E.1
-
6
-
-
84964809175
-
Gut microbiota and colorectal cancer
-
6 Yu, Y-N., Fang, J-Y., Gut microbiota and colorectal cancer. Gastrointest. Tumors 2 (2015), 26–32.
-
(2015)
Gastrointest. Tumors
, vol.2
, pp. 26-32
-
-
Yu, Y.-N.1
Fang, J.-Y.2
-
7
-
-
84866549438
-
Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment
-
7 Morgan, X.C., et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol., 13, 2012, R79.
-
(2012)
Genome Biol.
, vol.13
, pp. R79
-
-
Morgan, X.C.1
-
8
-
-
84890985732
-
Faecal microbiota composition and host–microbe cross-talk following gastroenteritis and in postinfectious irritable bowel syndrome
-
8 Jalanka-Tuovinen, J., et al. Faecal microbiota composition and host–microbe cross-talk following gastroenteritis and in postinfectious irritable bowel syndrome. Gut 63 (2014), 1737–1745.
-
(2014)
Gut
, vol.63
, pp. 1737-1745
-
-
Jalanka-Tuovinen, J.1
-
9
-
-
84973667684
-
Acetate mediates a microbiome–brain–β-cell axis to promote metabolic syndrome
-
9 Perry, R.J., et al. Acetate mediates a microbiome–brain–β-cell axis to promote metabolic syndrome. Nature 534 (2016), 213–217.
-
(2016)
Nature
, vol.534
, pp. 213-217
-
-
Perry, R.J.1
-
10
-
-
84856374839
-
Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae
-
10 Stecher, B., et al. Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae. Proc. Natl. Acad. Sci. U.S.A. 109 (2012), 1269–1274.
-
(2012)
Proc. Natl. Acad. Sci. U.S.A.
, vol.109
, pp. 1269-1274
-
-
Stecher, B.1
-
11
-
-
84951912603
-
The microbiome: the trillions of microorganisms that maintain health and cause disease in humans and companion animals
-
11 Rodrigues Hoffmann, A., et al. The microbiome: the trillions of microorganisms that maintain health and cause disease in humans and companion animals. Vet. Pathol. 53 (2016), 10–21.
-
(2016)
Vet. Pathol.
, vol.53
, pp. 10-21
-
-
Rodrigues Hoffmann, A.1
-
12
-
-
84925855673
-
The gut microbiome in cardio-metabolic health
-
12 Hansen, T.H., et al. The gut microbiome in cardio-metabolic health. Genome Med., 7, 2015, 33.
-
(2015)
Genome Med.
, vol.7
, pp. 33
-
-
Hansen, T.H.1
-
13
-
-
84878450664
-
Interpreting infective microbiota: the importance of an ecological perspective
-
13 Rogers, G.B., et al. Interpreting infective microbiota: the importance of an ecological perspective. Trends Microbiol. 21 (2013), 271–276.
-
(2013)
Trends Microbiol.
, vol.21
, pp. 271-276
-
-
Rogers, G.B.1
-
14
-
-
84896863130
-
Bacterial multispecies studies and microbiome analysis of a plant disease
-
14 Passos da Silva, D., et al. Bacterial multispecies studies and microbiome analysis of a plant disease. Microbiol. Read. Engl. 160 (2014), 556–566.
-
(2014)
Microbiol. Read. Engl.
, vol.160
, pp. 556-566
-
-
Passos da Silva, D.1
-
15
-
-
80455145221
-
An in vivo polymicrobial biofilm wound infection model to study interspecies interactions
-
15 Dalton, T., et al. An in vivo polymicrobial biofilm wound infection model to study interspecies interactions. PloS One, 6, 2011, e27317.
-
(2011)
PloS One
, vol.6
, pp. e27317
-
-
Dalton, T.1
-
16
-
-
84895190999
-
Mechanisms of synergy in polymicrobial infections
-
16 Murray, J.L., et al. Mechanisms of synergy in polymicrobial infections. J. Microbiol. Seoul Korea 52 (2014), 188–199.
-
(2014)
J. Microbiol. Seoul Korea
, vol.52
, pp. 188-199
-
-
Murray, J.L.1
-
17
-
-
20144380698
-
Network thinking in ecology and evolution
-
17 Proulx, S.R., et al. Network thinking in ecology and evolution. Trends Ecol. Evol. 20 (2005), 345–353.
-
(2005)
Trends Ecol. Evol.
, vol.20
, pp. 345-353
-
-
Proulx, S.R.1
-
18
-
-
84863920287
-
Microbial interactions: from networks to models
-
18 Faust, K., Raes, J., Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10 (2012), 538–550.
-
(2012)
Nat. Rev. Microbiol.
, vol.10
, pp. 538-550
-
-
Faust, K.1
Raes, J.2
-
19
-
-
84864579031
-
Microbial co-occurrence relationships in the human microbiome
-
19 Faust, K., et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol., 8, 2012, e1002606.
-
(2012)
PLoS Comput. Biol.
, vol.8
, pp. e1002606
-
-
Faust, K.1
-
20
-
-
84929997716
-
Determinants of community structure in the global plankton interactome
-
20 Lima-Mendez, G., et al. Determinants of community structure in the global plankton interactome. Science, 348, 2015, 1262073.
-
(2015)
Science
, vol.348
, pp. 1262073
-
-
Lima-Mendez, G.1
-
21
-
-
85027927719
-
Enterotypes of the human gut microbiome
-
21 Arumugam, M., et al. Enterotypes of the human gut microbiome. Nature 473 (2011), 174–180.
-
(2011)
Nature
, vol.473
, pp. 174-180
-
-
Arumugam, M.1
-
22
-
-
84855949949
-
Using network analysis to explore co-occurrence patterns in soil microbial communities
-
22 Barberán, A., et al. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6 (2012), 343–351.
-
(2012)
ISME J.
, vol.6
, pp. 343-351
-
-
Barberán, A.1
-
23
-
-
84923197818
-
Seasonal community succession of the phyllosphere microbiome
-
23 Copeland, J.K., et al. Seasonal community succession of the phyllosphere microbiome. Mol. Plant. Microbe Interact. 28 (2015), 274–285.
-
(2015)
Mol. Plant. Microbe Interact.
, vol.28
, pp. 274-285
-
-
Copeland, J.K.1
-
24
-
-
84991010752
-
A two-part mixed-effects model for analyzing longitudinal microbiome compositional data
-
24 Chen, E.Z., Li, H., A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics 32 (2016), 2611–2617.
-
(2016)
Bioinformatics
, vol.32
, pp. 2611-2617
-
-
Chen, E.Z.1
Li, H.2
-
25
-
-
84959106176
-
Correlation detection strategies in microbial data sets vary widely in sensitivity and precision
-
25 Weiss, S., et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10 (2016), 1669–1681.
-
(2016)
ISME J.
, vol.10
, pp. 1669-1681
-
-
Weiss, S.1
-
26
-
-
84867627559
-
Associations between pathogens in the upper respiratory tract of young children: interplay between viruses and bacteria
-
26 van den Bergh, M.R., et al. Associations between pathogens in the upper respiratory tract of young children: interplay between viruses and bacteria. PloS One, 7, 2012, e47711.
-
(2012)
PloS One
, vol.7
, pp. e47711
-
-
van den Bergh, M.R.1
-
27
-
-
84884397652
-
Integrative approaches for finding modular structure in biological networks
-
27 Mitra, K., et al. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14 (2013), 719–732.
-
(2013)
Nat. Rev. Genet.
, vol.14
, pp. 719-732
-
-
Mitra, K.1
-
28
-
-
33750177351
-
Centrality in social networks conceptual clarification
-
28 Freeman, L.C., Centrality in social networks conceptual clarification. Soc. Netw. 1 (1978), 215–239.
-
(1978)
Soc. Netw.
, vol.1
, pp. 215-239
-
-
Freeman, L.C.1
-
29
-
-
0035648637
-
A faster algorithm for betweenness centrality
-
29 Brandes, U., A faster algorithm for betweenness centrality. J. Math. Sociol. 25 (2001), 163–177.
-
(2001)
J. Math. Sociol.
, vol.25
, pp. 163-177
-
-
Brandes, U.1
-
30
-
-
84936824655
-
Power and centrality: a family of measures
-
30 Bonacich, P., Power and centrality: a family of measures. Am. J. Sociol. 92 (1987), 1170–1182.
-
(1987)
Am. J. Sociol.
, vol.92
, pp. 1170-1182
-
-
Bonacich, P.1
-
31
-
-
84867497055
-
Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach
-
31 Bauer, F., Lizier, J.T., Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach. EPL Europhys. Lett., 99, 2012, 68007.
-
(2012)
EPL Europhys. Lett.
, vol.99
, pp. 68007
-
-
Bauer, F.1
Lizier, J.T.2
-
32
-
-
84892962563
-
Epidemic centrality - is there an underestimated epidemic impact of network peripheral nodes?
-
32 Sikic, M., et al. Epidemic centrality - is there an underestimated epidemic impact of network peripheral nodes?. Eur. Phys. J. B, 86, 2013, 440.
-
(2013)
Eur. Phys. J. B
, vol.86
, pp. 440
-
-
Sikic, M.1
-
33
-
-
84905030166
-
Deciphering microbial interactions and detecting keystone species with co-occurrence networks
-
33 Berry, D., Widder, S., Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Microb. Symbioses, 5, 2014, 219.
-
(2014)
Microb. Symbioses
, vol.5
, pp. 219
-
-
Berry, D.1
Widder, S.2
-
34
-
-
84870810330
-
Accessibility in networks: A useful measure for understanding social insect nest architecture
-
34 Viana, M.P., et al. Accessibility in networks: A useful measure for understanding social insect nest architecture. Chaos Solitons Fractals 46 (2013), 38–45.
-
(2013)
Chaos Solitons Fractals
, vol.46
, pp. 38-45
-
-
Viana, M.P.1
-
35
-
-
84924043785
-
Understanding the spreading power of all nodes in a network
-
35 Lawyer, G., Understanding the spreading power of all nodes in a network. Sci. Rep., 5, 2014, 8665.
-
(2014)
Sci. Rep.
, vol.5
, pp. 8665
-
-
Lawyer, G.1
-
36
-
-
0038589165
-
The anatomy of a large-scale hypertextual web search engine
-
36 Brin, S., Page, L., The anatomy of a large-scale hypertextual web search engine. Proceedings of the Seventh International Conference on World Wide Web 7, Amsterdam, 1998, 107–117.
-
(1998)
Proceedings of the Seventh International Conference on World Wide Web 7, Amsterdam
, pp. 107-117
-
-
Brin, S.1
Page, L.2
-
37
-
-
4243148480
-
Authoritative sources in a hyperlinked environment
-
37 Kleinberg, J.M., Authoritative sources in a hyperlinked environment. JACM 46 (1999), 604–632.
-
(1999)
JACM
, vol.46
, pp. 604-632
-
-
Kleinberg, J.M.1
-
38
-
-
84879744885
-
The long-term stability of the human gut microbiota
-
38 Faith, J.J., et al. The long-term stability of the human gut microbiota. Science, 341, 2013, 1237439.
-
(2013)
Science
, vol.341
, pp. 1237439
-
-
Faith, J.J.1
-
39
-
-
84865477413
-
Antibiotics in early life alter the murine colonic microbiome and adiposity
-
39 Cho, I., et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488 (2012), 621–626.
-
(2012)
Nature
, vol.488
, pp. 621-626
-
-
Cho, I.1
-
40
-
-
84892828465
-
Diet rapidly and reproducibly alters the human gut microbiome
-
40 David, L.A., et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505 (2014), 559–563.
-
(2014)
Nature
, vol.505
, pp. 559-563
-
-
David, L.A.1
-
41
-
-
84872568885
-
Efficient statistical significance approximation for local similarity analysis of high-throughput time series data
-
41 Xia, L.C., et al. Efficient statistical significance approximation for local similarity analysis of high-throughput time series data. Bioinforma. Oxf. Engl. 29 (2013), 230–237.
-
(2013)
Bioinforma. Oxf. Engl.
, vol.29
, pp. 230-237
-
-
Xia, L.C.1
-
42
-
-
84870506713
-
Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
-
42 Xia, L.C., et al. Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Syst. Biol. 5 (2011), 1–12.
-
(2011)
BMC Syst. Biol.
, vol.5
, pp. 1-12
-
-
Xia, L.C.1
-
43
-
-
33749824905
-
Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors
-
43 Ruan, Q., et al. Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors. Bioinforma. Oxf. Engl. 22 (2006), 2532–2538.
-
(2006)
Bioinforma. Oxf. Engl.
, vol.22
, pp. 2532-2538
-
-
Ruan, Q.1
-
44
-
-
84855951093
-
Coherent dynamics and association networks among lake bacterioplankton taxa
-
44 Eiler, A., et al. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6 (2012), 330–342.
-
(2012)
ISME J.
, vol.6
, pp. 330-342
-
-
Eiler, A.1
-
45
-
-
84957566691
-
Longitudinal prediction of the infant gut microbiome with dynamic Bayesian networks
-
45 McGeachie, M.J., et al. Longitudinal prediction of the infant gut microbiome with dynamic Bayesian networks. Sci. Rep., 6, 2016, 20359.
-
(2016)
Sci. Rep.
, vol.6
, pp. 20359
-
-
McGeachie, M.J.1
-
46
-
-
84931265628
-
Metagenomics meets time series analysis: unraveling microbial community dynamics
-
46 Faust, K., et al. Metagenomics meets time series analysis: unraveling microbial community dynamics. Curr. Opin. Microbiol. 25 (2015), 56–66.
-
(2015)
Curr. Opin. Microbiol.
, vol.25
, pp. 56-66
-
-
Faust, K.1
-
47
-
-
84890089590
-
Bayesian Gaussian copula factor models for mixed data
-
47 Murray, J.S., et al. Bayesian Gaussian copula factor models for mixed data. J. Am. Stat. Assoc. 108 (2013), 656–665.
-
(2013)
J. Am. Stat. Assoc.
, vol.108
, pp. 656-665
-
-
Murray, J.S.1
-
49
-
-
84889679014
-
Discovering disease-disease associations by fusing systems-level molecular data
-
49 Žitnik, M., et al. Discovering disease-disease associations by fusing systems-level molecular data. Sci. Rep., 3, 2013, 3202.
-
(2013)
Sci. Rep.
, vol.3
, pp. 3202
-
-
Žitnik, M.1
-
50
-
-
84946046702
-
Matrix factorization-based data fusion for drug-induced liver injury prediction
-
50 Žitnik, M., Zupan, B., Matrix factorization-based data fusion for drug-induced liver injury prediction. Syst. Biomed. 2 (2014), 16–22.
-
(2014)
Syst. Biomed.
, vol.2
, pp. 16-22
-
-
Žitnik, M.1
Zupan, B.2
-
51
-
-
84931037666
-
Gene network inference by fusing data from diverse distributions
-
51 Žitnik, M., Zupan, B., Gene network inference by fusing data from diverse distributions. Bioinformatics 31 (2015), i230–i239.
-
(2015)
Bioinformatics
, vol.31
, pp. i230-i239
-
-
Žitnik, M.1
Zupan, B.2
-
53
-
-
0038483826
-
Emergence of scaling in random networks
-
53 Barabási, A-L., Albert, R., Emergence of scaling in random networks. Science 286 (1999), 509–512.
-
(1999)
Science
, vol.286
, pp. 509-512
-
-
Barabási, A.-L.1
Albert, R.2
-
54
-
-
84938584646
-
Apoptosis regulatory protein–protein interaction demonstrates hierarchical scale-free fractal network
-
54 Nafis, S., et al. Apoptosis regulatory protein–protein interaction demonstrates hierarchical scale-free fractal network. Brief. Bioinform. 16 (2014), 675–699.
-
(2014)
Brief. Bioinform.
, vol.16
, pp. 675-699
-
-
Nafis, S.1
-
55
-
-
84929069926
-
Increased signaling entropy in cancer requires the scale-free property of protein interaction networks
-
55 Teschendorff, A.E., et al. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks. Sci. Rep., 5, 2015, 9646.
-
(2015)
Sci. Rep.
, vol.5
, pp. 9646
-
-
Teschendorff, A.E.1
-
56
-
-
84924181828
-
Towards a theory of Scale-free graphs: definition, properties, and implications
-
56 Li, L., et al. Towards a theory of Scale-free graphs: definition, properties, and implications. Internet Math. 2 (2005), 431–523.
-
(2005)
Internet Math.
, vol.2
, pp. 431-523
-
-
Li, L.1
-
57
-
-
0032482432
-
Collective dynamics of “small-world” networks
-
57 Watts, D.J., Strogatz, S.H., Collective dynamics of “small-world” networks. Nature 393 (1998), 440–442.
-
(1998)
Nature
, vol.393
, pp. 440-442
-
-
Watts, D.J.1
Strogatz, S.H.2
-
58
-
-
44349168786
-
Network “small-world-ness”: a quantitative method for determining canonical network equivalence
-
58 Humphries, M.D., Gurney, K., Network “small-world-ness”: a quantitative method for determining canonical network equivalence. PLoS One, 3, 2008, e2051.
-
(2008)
PLoS One
, vol.3
, pp. e2051
-
-
Humphries, M.D.1
Gurney, K.2
-
59
-
-
35948931439
-
An Arabidopsis gene network based on the graphical Gaussian model
-
59 Ma, S., et al. An Arabidopsis gene network based on the graphical Gaussian model. Genome Res. 17 (2007), 1614–1625.
-
(2007)
Genome Res.
, vol.17
, pp. 1614-1625
-
-
Ma, S.1
-
60
-
-
30044444291
-
Low-order conditional independence graphs for inferring genetic networks
-
60 Wille, A., Bühlmann, P., Low-order conditional independence graphs for inferring genetic networks. Stat. Appl. Genet. Mol. Biol., 5, 2006, 1.
-
(2006)
Stat. Appl. Genet. Mol. Biol.
, vol.5
, pp. 1
-
-
Wille, A.1
Bühlmann, P.2
-
61
-
-
84930608352
-
Sparse and compositionally robust inference of microbial ecological networks
-
61 Kurtz, Z.D., et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol., 11, 2015, e1004226.
-
(2015)
PLoS Comput. Biol.
, vol.11
, pp. e1004226
-
-
Kurtz, Z.D.1
-
62
-
-
84865733148
-
Inferring correlation networks from genomic survey data
-
62 Friedman, J., Alm, E.J., Inferring correlation networks from genomic survey data. PLoS Comput. Biol., 8, 2012, e1002687.
-
(2012)
PLoS Comput. Biol.
, vol.8
, pp. e1002687
-
-
Friedman, J.1
Alm, E.J.2
-
63
-
-
84957999071
-
A lipid-accumulating alga maintains growth in outdoor, alkaliphilic raceway pond with mixed microbial communities
-
63 Bell, T.A.S., et al. A lipid-accumulating alga maintains growth in outdoor, alkaliphilic raceway pond with mixed microbial communities. Microbiotechnology Ecotoxicol. Bioremediation, 6, 2016, 1480.
-
(2016)
Microbiotechnology Ecotoxicol. Bioremediation
, vol.6
, pp. 1480
-
-
Bell, T.A.S.1
-
64
-
-
84897446616
-
A fair comparison
-
359-3489
-
64 Costea, P.I., et al. A fair comparison. Nat. Methods, 11, 2014 359-3489.
-
(2014)
Nat. Methods
, vol.11
-
-
Costea, P.I.1
-
65
-
-
85010951420
-
CoNet app: inference of biological association networks using Cytoscape
-
65 Faust, K., Raes, J., CoNet app: inference of biological association networks using Cytoscape. F1000Research, 5, 2016, 1519.
-
(2016)
F1000Research
, vol.5
, pp. 1519
-
-
Faust, K.1
Raes, J.2
-
66
-
-
84946829739
-
Cross-biome comparison of microbial association networks
-
66 Faust, K., et al. Cross-biome comparison of microbial association networks. Syst. Microbiol., 6, 2015, 1200.
-
(2015)
Syst. Microbiol.
, vol.6
, pp. 1200
-
-
Faust, K.1
-
67
-
-
84946878516
-
Investigating microbial co-occurrence patterns based on metagenomic compositional data
-
67 Ban, Y., et al. Investigating microbial co-occurrence patterns based on metagenomic compositional data. Bioinformatics 31 (2015), 3322–3329.
-
(2015)
Bioinformatics
, vol.31
, pp. 3322-3329
-
-
Ban, Y.1
-
68
-
-
84943379443
-
CCLasso: correlation inference for compositional data through Lasso
-
68 Fang, H., et al. CCLasso: correlation inference for compositional data through Lasso. Bioinformatics 31 (2015), 3172–3180.
-
(2015)
Bioinformatics
, vol.31
, pp. 3172-3180
-
-
Fang, H.1
-
69
-
-
84861557467
-
Molecular ecological network analyses
-
69 Deng, Y., et al. Molecular ecological network analyses. BMC Bioinformatics, 13, 2012, 113.
-
(2012)
BMC Bioinformatics
, vol.13
, pp. 113
-
-
Deng, Y.1
-
70
-
-
84926327586
-
Learning microbial interaction networks from metagenomic count data
-
T.M. Przytycka Springer
-
70 Biswas, S., et al. Learning microbial interaction networks from metagenomic count data. Przytycka, T.M., (eds.) Research in Computational Molecular Biology, 2015, Springer, 32–43.
-
(2015)
Research in Computational Molecular Biology
, pp. 32-43
-
-
Biswas, S.1
-
71
-
-
0037062448
-
Community structure in social and biological networks
-
71 Girvan, M., Newman, M.E.J., Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99 (2002), 7821–7826.
-
(2002)
Proc. Natl. Acad. Sci. U.S.A.
, vol.99
, pp. 7821-7826
-
-
Girvan, M.1
Newman, M.E.J.2
-
72
-
-
33646530046
-
Computing communities in large networks using random walks
-
72 Pons, P., Latapy, M., Computing communities in large networks using random walks. Proceedings of the 20th International Conference on Computer and Information Sciences, Berlin, Heidelberg, 2005, 284–293.
-
(2005)
Proceedings of the 20th International Conference on Computer and Information Sciences, Berlin, Heidelberg
, pp. 284-293
-
-
Pons, P.1
Latapy, M.2
-
73
-
-
56349094785
-
Fast unfolding of communities in large networks
-
73 Blondel, V.D., et al. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp., 2008, 2008, P10008.
-
(2008)
J. Stat. Mech. Theory Exp.
, vol.2008
, pp. P10008
-
-
Blondel, V.D.1
-
74
-
-
61849113253
-
Graph clustering via a discrete uncoupling process
-
74 Van Dongen, S., Graph clustering via a discrete uncoupling process. SIAM J. Matrix Anal. Appl. 30 (2008), 121–141.
-
(2008)
SIAM J. Matrix Anal. Appl.
, vol.30
, pp. 121-141
-
-
Van Dongen, S.1
-
75
-
-
84885828125
-
Epigenomic programming contributes to the genomic drift evolution of the F-Box protein superfamily in Arabidopsis
-
75 Hua, Z., et al. Epigenomic programming contributes to the genomic drift evolution of the F-Box protein superfamily in Arabidopsis. Proc. Natl. Acad. Sci. U.S.A. 110 (2013), 16927–16932.
-
(2013)
Proc. Natl. Acad. Sci. U.S.A.
, vol.110
, pp. 16927-16932
-
-
Hua, Z.1
-
76
-
-
84902955557
-
Phylogenomics and the dynamic genome evolution of the genus Streptococcus
-
76 Richards, V.P., et al. Phylogenomics and the dynamic genome evolution of the genus Streptococcus. Genome Biol. Evol. 6 (2014), 741–753.
-
(2014)
Genome Biol. Evol.
, vol.6
, pp. 741-753
-
-
Richards, V.P.1
-
77
-
-
84949671129
-
Protein complex identification through Markov clustering with firefly algorithm on dynamic protein–protein interaction networks
-
77 Lei, X., et al. Protein complex identification through Markov clustering with firefly algorithm on dynamic protein–protein interaction networks. Inf. Sci. 329 (2016), 303–316.
-
(2016)
Inf. Sci.
, vol.329
, pp. 303-316
-
-
Lei, X.1
-
78
-
-
84872735831
-
Construction and application of dynamic protein interaction network based on time course gene expression data
-
78 Wang, J., et al. Construction and application of dynamic protein interaction network based on time course gene expression data. PROTEOMICS 13 (2013), 301–312.
-
(2013)
PROTEOMICS
, vol.13
, pp. 301-312
-
-
Wang, J.1
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