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A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

Artikel i vetenskaplig tidskrift
Författare Oveis Jamialahmadi
Sameereh Hashemi-Najafabadi
Ehsan Motamedian
Stefano Romeo
Fatemeh Bagheri
Publicerad i PLoS computational biology
Volym 15
Nummer/häfte 4
Sidor e1006936
ISSN 1553-7358
Publiceringsår 2019
Publicerad vid Institutionen för medicin, avdelningen för molekylär och klinisk medicin
Sidor e1006936
Språk en
Länkar dx.doi.org/10.1371/journal.pcbi.100...
www.ncbi.nlm.nih.gov/entrez/query.f...
Ämneskategorier Medicinsk informatik

Sammanfattning

Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.

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http://gu.se/forskning/publikation/?publicationId=280223
Utskriftsdatum: 2019-10-20