Partial AUC maximization for essential gene prediction using genetic algorithms

Hwang, Kyu-Baek; Ha, Beom-Yong; Ju, Sanghun; Kim, Sang Soo

doi:10.5483/bmbrep.2013.46.1.159

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…proposed a method based on genetic algorithms to predict essential genes of S . cerevisiae , with a backward search-based wrapper for feature selection amongst 31 features [ 10 ]. Plaimas et al .…”

Section: Introductionmentioning

confidence: 99%

Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species

et al. 2017

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Genes that are indispensable for survival are essential genes. Many features have been proposed for computational prediction of essential genes. In this paper, the least absolute shrinkage and selection operator method was used to screen key sequence-based features related to gene essentiality. To assess the effects, the selected features were used to predict the essential genes from 31 bacterial species based on a support vector machine classifier. For all 31 bacterial objects (21 Gram-negative objects and ten Gram-positive objects), the features in the three datasets were reduced from 57, 59, and 58, to 40, 37, and 38, respectively, without loss of prediction accuracy. Results showed that some features were redundant for gene essentiality, so could be eliminated from future analyses. The selected features contained more complex (or key) biological information for gene essentiality, and could be of use in related research projects, such as gene prediction, synthetic biology, and drug design.

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Section: Introductionmentioning

confidence: 99%

Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species

et al. 2017

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“…An important benefit of constructing linear biomarker combinations by targeting the performance measure of interest is that the performance of the combination will be at least as good as the performance of the individual biomarkers (Pepe, Cai, and Longton, 2006). Indeed, several authors have recommended matching the objective function to the performance measure, i.e., constructing biomarker combinations by optimizing the relevant measure of performance (Hwang et al, 2013;Liu, Schisterman, and Zhu, 2005;Wang and Chang, 2011;Ricamato and Tortorella, 2011). To that end, we propose a distribution-free method to construct biomarker combinations by maximizing the TPR for a given FPR.…”

Section: Biomarker Combinationsmentioning

confidence: 99%

Combining Biomarkers by Maximizing the True Positive Rate for a Fixed False Positive Rate

Meisner¹,

Carone²,

Pepe³

et al. 2019

Preprint

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Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate for a biomarker combination at a prespecified, clinically acceptable false positive rate is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the true positive rate while constraining the false positive rate. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations. Biomarker combinations; False positive rate; Sensitivity; Specificity; True positive rate.

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“…Fourth, different theoretical guarantees have been examined, e.g., consistency [45], generalization error bounds [84], excess risk bounds [54,170], regret bounds [178], convergence rates or sample complexities [89], stability [85,163]. Last but not least, AUC maximization has been successfully investigated in a variety of applications [7,10,40,57,69,132,135,146,147,156,181,184], e.g., medical image classification [173] and molecular properties prediction [151], to mention but a few.…”

Section: Introductionmentioning

confidence: 99%

AUC Maximization in the Era of Big Data and AI: A Survey

Yang¹,

Ying²

2022

Preprint

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Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization for deep learning have received increasing attention and yielded great impact for solving real-world problems. However, to the best our knowledge there is no comprehensive survey of related works for AUC maximization. This paper aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for deep AUC maximization, and provide suggestions on topics for future work.

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Partial AUC maximization for essential gene prediction using genetic algorithms

Cited by 9 publications

References 23 publications

Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species

Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species

Combining Biomarkers by Maximizing the True Positive Rate for a Fixed False Positive Rate

AUC Maximization in the Era of Big Data and AI: A Survey

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