publication . Part of book or chapter of book . Article . 2020

Network Aggregation to Enhance Results Derived from Multiple Analytics

Diane Duroux; Héctor Climente-González; Lars Wienbrandt; Kristel Van Steen;
Open Access
  • Published: 29 May 2020
  • Publisher: Springer, Cham
Abstract
The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single ...
Persistent Identifiers
Subjects
free text keywords: Article, Networks, Aggregation, Latent class methods, Epistasis, Complex data type, Aggregation methods, Data type, Epistasis, Computational biology, Computer science, Analytics, business.industry, business, Positive control, Single-nucleotide polymorphism, Genetic association
Related Organizations
Funded by
EC| MLFPM2018
Project
MLFPM2018
Machine Learning Frontiers in Precision Medicine
  • Funder: European Commission (EC)
  • Project Code: 813533
  • Funding stream: H2020 | MSCA-ITN-ETN
Validated by funder
Download fromView all 4 versions
Open Access
https://link.springer.com/cont...
Part of book or chapter of book
Provider: UnpayWall
Open Access
ZENODO
Part of book or chapter of book . 2020
Provider: ZENODO
Any information missing or wrong?Report an Issue