Abstract:
BACKGROUND:Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize the defective pathways in phenotype prediction problems, such as the Fisher's ratio sampler, the Holdout sampler and the Random sampler, and apply each one to the analysis of genetic pathways involved in tumor behavior and outcomes of triple negative breast cancers (TNBC). Altered biological pathways are identified using the most frequently sampled genes and are compared to those obtained via Bayesian Networks (BNs). RESULTS:Random, Fisher's ratio and Holdout samplers were more accurate and robust than BNs, while providing comparable insights about disease genomics. CONCLUSIONS:The three samplers tested are good alternatives to Bayesian Networks since they are less computationally demanding algorithms. Importantly, this analysis confirms the concept of "biological invariance" since the altered pathways should be independent of the sampling methodology and the classifier used for their inference. Nevertheless, still some modifications are needed in the Bayesian networks to be able to sample correctly the uncertainty space in phenotype prediction problems, since the probabilistic parameterization of the uncertainty space is not unique and the use of the optimum network might falsify the pathways analysis.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Cernea A,Fernández-Martínez JL,deAndrés-Galiana EJ,Fernández-Ovies FJ,Alvarez-Machancoses O,Fernández-Muñiz Z,Saligan LN,Sonis STdoi
10.1186/s12859-020-3356-6subject
Has Abstractpub_date
2020-03-11 00:00:00pages
89issue
Suppl 2issn
1471-2105pii
10.1186/s12859-020-3356-6journal_volume
21pub_type
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