FERAL: network-based classifier with application to breast cancer outcome prediction

Allahyar, Amin; Ridder, Jeroen de

doi:10.1093/bioinformatics/btv255

Cited by 41 publications

(51 citation statements)

References 41 publications

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“…Unfortunately, this cannot be naively assumed to be true. 31 As with Venet et al's work on genomic signatures for breast cancer outcome, 29 we observe a similar phenomenon in proteomics (Tables 1C and 2C). By the CVAccuracy alone, it would appear that any method is good for class prediction.…”

Section: In Proteomics Standard Feature-selection Approaches Do No Bsupporting

confidence: 86%

Evaluating feature-selection stability in next-generation proteomics

Goh

Wong

2016

J. Bioinform. Comput. Biol.

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Identifying reproducible yet relevant features is a major challenge in biological research. This is well documented in genomics data. Using a proposed set of three reliability benchmarks, we find that this issue exists also in proteomics for commonly used feature-selection methods, e.g. [Formula: see text]-test and recursive feature elimination. Moreover, due to high test variability, selecting the top proteins based on [Formula: see text]-value ranks - even when restricted to high-abundance proteins - does not improve reproducibility. Statistical testing based on networks are believed to be more robust, but this does not always hold true: The commonly used hypergeometric enrichment that tests for enrichment of protein subnets performs abysmally due to its dependence on unstable protein pre-selection steps. We demonstrate here for the first time the utility of a novel suite of network-based algorithms called ranked-based network algorithms (RBNAs) on proteomics. These have originally been introduced and tested extensively on genomics data. We show here that they are highly stable, reproducible and select relevant features when applied to proteomics data. It is also evident from these results that use of statistical feature testing on protein expression data should be executed with due caution. Careless use of networks does not resolve poor-performance issues, and can even mislead. We recommend augmenting statistical feature-selection methods with concurrent analysis on stability and reproducibility to improve the quality of the selected features prior to experimental validation.

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Section: In Proteomics Standard Feature-selection Approaches Do No Bsupporting

confidence: 86%

Evaluating feature-selection stability in next-generation proteomics

Goh

Wong

2016

J. Bioinform. Comput. Biol.

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“…A single step variance based gene identification method has been discussed in 12 . The method collects the event data from united state firms.…”

Section: Methods Exploredmentioning

confidence: 99%

Untitled

2017

IJPSR

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Proteins are the most essential and versatile macromolecules of life, and the knowledge of their functions is a crucial link in the development of new drugs, better crops, and even the development of synthetic biochemicals such as biofuels. Experimental procedures for protein function prediction are inherently low throughput and are thus unable to annotate a non-trivial fraction of proteins that are becoming available due to rapid advances in genome sequencing technology. This has motivated the development of computational techniques that utilize a variety of highthroughput experimental data for protein function prediction, such as protein and genome sequences, gene expression data, protein interaction networks and phylogenetic profiles.

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“…Compared with the survival analysis results in the 349 original study [56], we achieved more significant sample stratifications with EMT [22,24,35]. Some accredit this to the existence of a large number of genes 357 that are correlated with the target labels [12]. Given the limited amount of samples, it 358 becomes very hard to differentiate the marker genes and irrelevant genes.…”

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confidence: 86%

EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma

Shao

Bjaanæs

Helland

et al. 2018

Preprint

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Various feature selection algorithms have been proposed to identify cancer prognostic biomarkers. In recent years, however, their reproducibility is criticized. The performance of feature selection algorithms is shown to be affected by the datasets, underlying networks and evaluation metrics. One of the causes is the curse of dimensionality, which makes it hard to select the features that generalize well on independent data. Even the integration of biological networks does not mitigate this issue because the networks are large and many of their components are not relevant for the phenotype of interest. With the availability of multi-omics data, integrative approaches are being developed to build more robust predictive models. In this scenario, the higher data dimensions create greater challenges.We proposed a phenotype relevant network-based feature selection (PRNFS) framework and demonstrated its advantages in lung cancer prognosis prediction. We constructed cancer prognosis relevant networks based on epithelial mesenchymal transition (EMT) and integrated them with different types of omics data for feature selection. With less than 2.5% of the total dimensionality, we obtained EMT prognostic signatures that achieved remarkable prediction performance (average AUC values >0.8), very significant sample stratifications, and meaningful biological interpretations. In addition to finding EMT signatures from different omics data levels, we combined these single-omics signatures into multi-omics signatures, which improved sample stratifications significantly. Both single-and multi-omics EMT signatures were tested on independent multi-omics lung cancer datasets and significant sample stratifications were obtained.

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FERAL: network-based classifier with application to breast cancer outcome prediction

Cited by 41 publications

References 41 publications

Evaluating feature-selection stability in next-generation proteomics

Evaluating feature-selection stability in next-generation proteomics

Untitled

EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma

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