Gene Network Inference.pdf

Gene Network Inference

This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.

Gene Regulatory Network Inference from Single … We also infer gene regulatory networks from three experimental single-cell datasets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available. PIDC should facilitate the identification of putative functional

7.24 MB DATEIGRÖSSE
3642451608 ISBN
Englisch SPRACHE
Gene Network Inference.pdf

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Sofia Voigt

Gene network inference by fusing data from diverse distributions Marinka Zitnik! 1 and Blazˇ Zupan1,2,* 1Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

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Matteo Müller

Computational biology and gene network …

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Noel Schulze

The inference of a gene regulation network requires knowledge of the gene expression levels at successive time points, at least before and after a network transition. However, owing to experimental limitations a complete determination of the precursor state is not possible. Results: We investigate a strategy for the inference of gene regulatory networks from incomplete expression data based on

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Jason Lehmann

Gene regulatory network inference resources: A …

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Jessica Kohmann

02.07.2017 · Background The inference of gene regulatory networks is of great interest and has various applications. The recent advances in high-throughout biological data collection have facilitated the construction and understanding of gene regulatory networks in many model organisms. However, the inference of gene networks from large-scale human genomic data can be challenging. Generally, it is