Titlebook: Computational Stem Cell Biology; Methods and Protocol Patrick Cahan Book 2019 Springer Science+Business Media, LLC, part of Springer Nature

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書目名稱Computational Stem Cell Biology影響因子(影響力)




書目名稱Computational Stem Cell Biology影響因子(影響力)學科排名




書目名稱Computational Stem Cell Biology網絡公開度




書目名稱Computational Stem Cell Biology網絡公開度學科排名




書目名稱Computational Stem Cell Biology被引頻次




書目名稱Computational Stem Cell Biology被引頻次學科排名




書目名稱Computational Stem Cell Biology年度引用




書目名稱Computational Stem Cell Biology年度引用學科排名




書目名稱Computational Stem Cell Biology讀者反饋




書目名稱Computational Stem Cell Biology讀者反饋學科排名

magnate

Tailor

Sustainable Tertiary Education in Asialusters of densely connected cells. This subpopulation structure represents a map of putative cell-state transitions. CellRouter implements a flow network algorithm to explore this map and reconstruct cell-state transitions in complex single-cell, multidimensional omics datasets. We describe a step-

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André Matthes,Katja Beyer,Anton Schumannl biologists. We focus specifically on the use of one tool, called Iterative Clustering and Guide-gene Selection (ICGS), which has been shown to uncover novel committed, transitional, and metastable progenitor cell states. As a component of the AltAnalyze toolkit, ICGS provides advanced methods to e

有毛就脫毛

Modeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks GRN can enable control of the eventual cell fates with potential clinical applications. In this chapter, we describe our computational methodologies that we have tailor-made with purpose of applications to cell fate control. Briefly, we introduce the process of cellular differentiation and reprogra

規(guī)章

Computational Tools for Quantifying Concordance in Single-Cell Fateifetime and to model the deterministic effect of cell environment and inheritance, i.e., nature versus nurture. We have applied competing risks statistics, a branch of survival statistics, to quantify cell fate concordance from cell lifetime data. Competing risks modelling of cell fate concordance p

規(guī)章

碎石頭

Investigating Cell Fate Decisions with ICGS Analysis of Single Cellsl biologists. We focus specifically on the use of one tool, called Iterative Clustering and Guide-gene Selection (ICGS), which has been shown to uncover novel committed, transitional, and metastable progenitor cell states. As a component of the AltAnalyze toolkit, ICGS provides advanced methods to e

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背心

https://doi.org/10.1007/978-1-4939-9224-9gene regulatory networks; single-cell transcriptome data; teratomas; engineered cells; fetal tissues