BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

419 related articles for article (PubMed ID: 31348484)

  • 1. Influence of network properties on a migration induced secular height trend by Monte Carlo simulation.
    Fritz A; Makeyeva A; Staub K; Groth D
    Anthropol Anz; 2019 Nov; 76(5):433-443. PubMed ID: 31348484
    [No Abstract]   [Full Text] [Related]  

  • 2. Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.
    Groth D
    Anthropol Anz; 2017 Apr; 74(1):81-88. PubMed ID: 28362024
    [No Abstract]   [Full Text] [Related]  

  • 3. Monte Carlo simulation of body height in a spatial network.
    Hermanussen M; Aßmann C; Staub K; Groth D
    Eur J Clin Nutr; 2016 Jun; 70(6):671-8. PubMed ID: 27049032
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Extreme events and event size fluctuations in biased random walks on networks.
    Kishore V; Santhanam MS; Amritkar RE
    Phys Rev E Stat Nonlin Soft Matter Phys; 2012 May; 85(5 Pt 2):056120. PubMed ID: 23004834
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The average height of 18- and 19-year-old conscripts (N=458,322) in Switzerland from 1992 to 2009, and the secular height trend since 1878.
    Staub K; Rühli F; Woitek U; Pfister C
    Swiss Med Wkly; 2011; 141():w13238. PubMed ID: 21805409
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The impact of physical connectedness on body height in Swiss conscripts.
    Hermanussen M; Alt C; Staub K; Aßmann C; Groth D
    Anthropol Anz; 2014; 71(4):313-27. PubMed ID: 25774949
    [TBL] [Abstract][Full Text] [Related]  

  • 7. The end of the secular trend in Norway: spatial trends in body height of Norwegian conscripts in the 19
    Rybak A; Bents D; Krüger J; Groth D
    Anthropol Anz; 2020 Jun; 77(5):415-421. PubMed ID: 32588018
    [No Abstract]   [Full Text] [Related]  

  • 8. Spatial conscript body height correlation of Norwegian districts in the 19
    Bents D; Rybak A; Groth D
    Anthropol Anz; 2017 Apr; 74(1):65-69. PubMed ID: 28375426
    [No Abstract]   [Full Text] [Related]  

  • 9. Range-limited centrality measures in complex networks.
    Ercsey-Ravasz M; Lichtenwalter RN; Chawla NV; Toroczkai Z
    Phys Rev E Stat Nonlin Soft Matter Phys; 2012 Jun; 85(6 Pt 2):066103. PubMed ID: 23005158
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Efficient rewirings for enhancing synchronizability of dynamical networks.
    Rad AA; Jalili M; Hasler M
    Chaos; 2008 Sep; 18(3):037104. PubMed ID: 19045478
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A new measure of centrality for brain networks.
    Joyce KE; Laurienti PJ; Burdette JH; Hayasaka S
    PLoS One; 2010 Aug; 5(8):e12200. PubMed ID: 20808943
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The network effects on conscripts' height in the central provinces of Russian empire in the middle of XIX century - at the beginning of XX century.
    Lebedeva L; Groth D; Hermanussen M; Scheffler C; Godina E
    Anthropol Anz; 2019 Nov; 76(5):371-377. PubMed ID: 30994699
    [No Abstract]   [Full Text] [Related]  

  • 13. Towards a methodology for validation of centrality measures in complex networks.
    Batool K; Niazi MA
    PLoS One; 2014; 9(4):e90283. PubMed ID: 24709999
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Understanding the implementation of evidence-based care: a structural network approach.
    Parchman ML; Scoglio CM; Schumm P
    Implement Sci; 2011 Feb; 6():14. PubMed ID: 21349194
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The secular trend and network effects on height of male Japanese students from 1955 to 2015.
    Bents D; Groth D; Satake T
    Anthropol Anz; 2018 Jun; 74(5):423-429. PubMed ID: 29668008
    [No Abstract]   [Full Text] [Related]  

  • 16. Structure of shells in complex networks.
    Shao J; Buldyrev SV; Braunstein LA; Havlin S; Stanley HE
    Phys Rev E Stat Nonlin Soft Matter Phys; 2009 Sep; 80(3 Pt 2):036105. PubMed ID: 19905178
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Consistency and differences between centrality measures across distinct classes of networks.
    Oldham S; Fulcher B; Parkes L; Arnatkevic Iūtė A; Suo C; Fornito A
    PLoS One; 2019; 14(7):e0220061. PubMed ID: 31348798
    [TBL] [Abstract][Full Text] [Related]  

  • 18. New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.
    Riera-Fernández P; Munteanu CR; Escobar M; Prado-Prado F; Martín-Romalde R; Pereira D; Villalba K; Duardo-Sánchez A; González-Díaz H
    J Theor Biol; 2012 Jan; 293():174-88. PubMed ID: 22037044
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identifying highly influential nodes in the complicated grief network.
    Robinaugh DJ; Millner AJ; McNally RJ
    J Abnorm Psychol; 2016 Aug; 125(6):747-57. PubMed ID: 27505622
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Network-based control of epidemic via flattening the infection curve: high-clustered vs. low-clustered social networks.
    Doostmohammadian M; Rabiee HR
    Soc Netw Anal Min; 2023; 13(1):60. PubMed ID: 37033472
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.