Cross-Platform Omics Prediction procedure: a game changer for implementing precision medicine in patients with stage-III melanoma

Wang, Kevin Y.X.; Pupo, Gulietta M.; Patrick, Ellis; Strbenac, Dario; Schramm, Sarah-Jane; Thompson, John F.; Scolyer, Richard A.; Mueller, S.; Tarr, Garth; Mann, Graham J.; Yang, Pengyi

doi:10.1101/2020.12.09.415927

Cited by 4 publications

(4 citation statements)

References 55 publications

(50 reference statements)

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“…Metabolites analysed in this paper were calculated as metabolite-to-metabolite ratios for each metabolite in the dataset. This was done following the transferability concept proposed in 59 and self-normalising aspect of calculating ratios to reduce variation between batches ( Figure S6 ). The raw imputed metabolite data was first log transformed and then ratios between each metabolite were taken.…”

Section: Methodsmentioning

confidence: 99%

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Chan¹,

Wu²,

Vernon³

et al. 2023

iScience

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Section: Methodsmentioning

confidence: 99%

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Chan¹,

Wu²,

Vernon³

et al. 2023

iScience

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“…The itraq protein expression data has 41 patients with 640 proteins. The nanostring data has 45 patients with 204 genes [ 49 ], and the GEO ID is “GSE156030."…”

Section: Methodsmentioning

confidence: 99%

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

et al. 2022

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Survival analysis is a branch of statistics that deals with both the tracking of time and the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarize the survival time and perform a classification analysis. Here, we develop a benchmarking design, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics data sets. SurvBenchmark not only focuses on classical approaches such as the Cox model but also evaluates state-of-the-art machine learning survival models. All approaches were assessed using multiple performance metrics; these include model predictability, stability, flexibility, and computational issues. Our systematic comparison design with 320 comparisons (20 methods over 16 data sets) shows that the performances of survival models vary in practice over real-world data sets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.

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“…The itraq protein expression data has 41 patients with 640 proteins. The nanostring data has 45 patients with 204 genes [46], and the GEO ID is “GSE156030”.…”

Section: Methodsmentioning

confidence: 99%

“…We also conclude from these recent comparison studies that they are either within traditional models (KM method, CoxPH model) or within modern machine learning (ML) methods. With the emergence of different modelling approaches from various disciplines addressing challenges such as different model assumptions, a recent comprehensive survey article by Wang et al (Wang et al, 2019) summarizes three categories of statistical survival models and ML methods with a focus on theoretical mathematical detail. However, their study does not discuss practical implications of the various methods, in particular there is no comparison of performance using real world datasets.…”

Section: Introductionmentioning

confidence: 99%

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Zhang

Wong

Mann

et al. 2021

Preprint

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Survival analysis is a branch of statistics that deals with both, the tracking of time and of the survival status simultaneously as the dependent response. Current comparisons of the performance of survival models mostly focus on classical clinical data with traditional statistical survival models, with prediction accuracy being often the only measurement of model performance. Moreover, survival analysis approaches for censored omics data have not been fully studied. The typical solution is to truncate survival time, to define a new status variable, and to then perform a binary classification analysis.Here, we develop a benchmarking framework that compares survival models for both clinical datasets and omics datasets, and that not only focuses on classical statistical survival models but also incorporates state-of-art machine learning survival models with multiple performance evaluation measurements including model predictability, stability, flexibility and computational issues. Our comprehensive comparison framework shows that optimality is dataset and analysis method dependent. The key result is that there is no one size fits all solution for any of the criteria and any of the methods. Some methods with a high C-index suffer from computational exhaustion and instability. The implications of our framework give researchers an insight on how different survival model implementations vary over real world datasets. We highlight that care is needed when selecting methods and recommend specifically not to consider the C-index as the only performance evaluation metric as alternative metrics measure other performance aspects.Code availabilityhttps://github.com/SydneyBioX/SurvBenchmarkContactjean.yang@sydney.edu.au

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Cross-Platform Omics Prediction procedure: a game changer for implementing precision medicine in patients with stage-III melanoma

Cited by 4 publications

References 55 publications

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

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