Titlebook: Computational Systems Toxicology; Julia Hoeng,Manuel C. Peitsch Book 2015 Springer Science+Business Media New York 2015 Biological network

毛發(fā)

書目名稱Computational Systems Toxicology影響因子(影響力)




書目名稱Computational Systems Toxicology影響因子(影響力)學(xué)科排名




書目名稱Computational Systems Toxicology網(wǎng)絡(luò)公開度




書目名稱Computational Systems Toxicology網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computational Systems Toxicology被引頻次




書目名稱Computational Systems Toxicology被引頻次學(xué)科排名




書目名稱Computational Systems Toxicology年度引用




書目名稱Computational Systems Toxicology年度引用學(xué)科排名




書目名稱Computational Systems Toxicology讀者反饋




書目名稱Computational Systems Toxicology讀者反饋學(xué)科排名

Frisky

978-1-4939-5003-4Springer Science+Business Media New York 2015

Herd-Immunity

Computational Systems Toxicology978-1-4939-2778-4Series ISSN 1557-2153 Series E-ISSN 1940-6053

繞著哥哥問

狂亂

Marieke de Groot,Jos de Keijser, far from trivial to accurately represent all this knowledge in a format suitable for a wide range of computational analyses. Nevertheless, many research groups have taken up this challenge, inspired by the great value and potential of pathway databases. Nowadays, these databases are routinely used

BRIDE

https://doi.org/10.1007/978-3-031-43841-7at will contribute to a better biological understanding. Sophisticated computational methods have been developed to separate mathematically the biological signal from the noise in high-throughput datasets; however, visualizing and putting a signal into a relevant biological context using . knowledge

BRIDE

Ebct207

VERT

https://doi.org/10.1007/978-3-031-43841-7enge is to keep the models relevant and representative of current scientific knowledge. Harnessing community intelligence in knowledge curation holds great promise in dealing with the flood of biological information. Another important challenge is to be able to share and communicate networks with ot

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