Sampling Defective Pathways in Phenotype Prediction Problems via the Holdout Sampler

Fernández-Martínez, Juan Luis; Cernea, Ana; deAndrés-Galiana, Enrique J.; Fernández-Ovies, Francisco Javier; Fernández-Muñiz, Zulima; Álvarez-Machancoses, Óscar; Saligan, Leorey N.; Sonis, Stephen T.

doi:10.1007/978-3-319-78759-6_3

Cited by 16 publications

(13 citation statements)

References 17 publications

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“…The The workflow corresponding to the holdout sampler is shown in Figure 2 [19]. The holdout sampler determines for each holdout the small-scale genetic signature in the training dataset (75% of the total data) and its predictive accuracy is established using the We next compared two algorithms for gene prioritization in order to establish the altered genetic pathways.…”

Section: Methodsmentioning

confidence: 99%

“…Bayesian networks can be used to discover relations between genes via a directed acyclic graph (see for instance [16,17]), nevertheless this method is computationally expensive and do not take fully into account the uncertainty of the corresponding phenotype prediction problem [18]). Here, we compare two different novel algorithms to identify pathways in phenotype prediction problems showing its application to PD [18,19]. Mathematically, the Holdout sampler is also related to the data kit inversion procedure [20].…”

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confidence: 99%

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Sampling Defective Pathways in Parkinson Disease

Fernández-Martínez

deAndrés-Galiana

Cernea

et al. 2019

JMID

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Discrimination of case-control status based on gene expression differences has potential to identify novel pathways relevant to neurodegenerative diseases including Parkinson’s disease (PD). In this paper we applied two different novel algorithms to predict dysregulated pathways of gene expression across several different regions of the brain in PD and controls. The Fisher’s ratio sampler uses the Fisher’s ratio of the most discriminatory genes as prior probability distribution to sample the genetic networks and their likelihood (accuracy) was established via Leave-One-Out-Cross Validation (LOOCV). The holdout sampler finds the minimum-scale signatures corresponding to different random holdouts, establishing their likelihood using the validation dataset in each holdout. Phenotype prediction problems have by genesis a very high underdetermined character. We used both approaches to sample different lists of genes that optimally discriminate PD from controls and subsequently used gene ontology to identify pathways affected by disease. Both algorithms identified common pathways of Insulin signaling, FOXA1 Transcription Factor Network, HIF-1 Signaling, p53 Signaling and Chromatin Regulation/Acetylation. This analysis provides new therapeutic targets to treat PD.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Sampling Defective Pathways in Parkinson Disease

Fernández-Martínez

deAndrés-Galiana

Cernea

et al. 2019

JMID

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“…In this case, this methodology was designed to explore the uncertainty space in phenotype prediction. This algorithm was used in other disciplines and fields of technology to optimally sample the model parameters posterior distribution via the least squares fitting of different data bags [6,8,9]. Figure 3 shows the HS workflow.…”

Section: Sampling Algorithms Fisher's Ratio Sampler (Frs)mentioning

confidence: 99%

“…The first method is the Fisher's ratio sampler [5] that explores the defective pathways considering the discriminatory capacity of the differentially expressed genes according to their Fisher's ratio that provides the "a priori" sampling distribution of the high-discriminatory networks. The second sampling algorithm, known as Holdout sampler, is inspired by the bootstrapping technique [6,7]. This algorithm quantifies the likelihood of the high discriminatory genetic networks using k-NN classifier in a validation data set using the minimum-scale genetic signature found in the training set of each random holdout.…”

Section: Introductionmentioning

confidence: 99%

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer

et al. 2020

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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. L Ã ðgÞ: g ∈ R s →C ¼ f1; 2g ð 1Þ The simplest case is to divide the phenotype in healthy controls and disease samples, but others problems

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“…Energy is sampled via a Random Holdout Sampler to obtain the energy distribution of the mutant protein, which indicates uncertainty of the energy of a mutant. This sampler has been used earlier in different fields to assess the intrinsic uncertainty in the inverse problem and in various classification problems [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes

et al. 2019

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Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab experiments to get a better understanding of protein mutations is costly and time consuming, and because of huge number of possible mutations the need of computational methods that could accurately predict effects of amino acid mutations is of greatest importance. In this research, we present a robust methodology to predict the energy changes of a proteins upon mutations. The proposed prediction scheme is based on two step algorithm that is a Holdout Random Sampler followed by a neural network model for regression. The Holdout Random Sampler is utilized to analysis the energy change, the corresponding uncertainty, and to obtain a set of admissible energy changes, expressed as a cumulative distribution function. These values are further utilized to train a simple neural network model that can predict the energy changes. Results were blindly tested (validated) against experimental energy changes, giving Pearson correlation coefficients of 0.66 for Single Point Mutations and 0.77 for Multiple Point Mutations. These results confirm the successfulness of our method, since it outperforms majority of previous studies in this field.

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Sampling Defective Pathways in Phenotype Prediction Problems via the Holdout Sampler

Cited by 16 publications

References 17 publications

Sampling Defective Pathways in Parkinson Disease

Sampling Defective Pathways in Parkinson Disease

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer

Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes

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