AUC-based biomarker ensemble with an application on gene scores predicting low bone mineral density

Zhao, Xiang; Dai, Wei; Li, Y.; Tian, Lu

doi:10.1093/bioinformatics/btr516

Cited by 15 publications

(12 citation statements)

References 21 publications

(22 reference statements)

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“…Due to the discontinuity and non-convexity of empirical AUC, a widely used technique for circumventing the computational challenge is to approximate the empirical AUC with some pairwise convex surrogate loss function. [36][37][38][39][40] However, it usually necessitates pairwise comparisons between positive and negative instances, resulting in quadratic computational complexity. [41][42][43][44] To alleviate the computational burden associated with pairwise surrogate losses, several non-pairwise strongly proper losses, such as the exponential loss and squared hinge loss, have been proposed and shown to be consistent with the AUC maximization task.…”

Section: Empirical Auc and Its Surrogate Lossesmentioning

confidence: 99%

“…Due to the discontinuity and non‐convexity of empirical AUC, a widely used technique for circumventing the computational challenge is to approximate the empirical AUC with some pairwise convex surrogate loss function 36‐40 . However, it usually necessitates pairwise comparisons between positive and negative instances, resulting in quadratic computational complexity 41‐44 .…”

Section: Main Frameworkmentioning

confidence: 99%

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A classification for complex imbalanced data in disease screening and early diagnosis

Hsu

2022

Statistics in Medicine

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Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high-dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high-dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high-dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.

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Section: Empirical Auc and Its Surrogate Lossesmentioning

confidence: 99%

Section: Main Frameworkmentioning

confidence: 99%

A classification for complex imbalanced data in disease screening and early diagnosis

Hsu

2022

Statistics in Medicine

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“…The AUC score of 1.00 means that the method is effective to identify the protein families. In bioinformatics and medical studies, AUC score is really important [41][42][43]. If the AUC score of a classifier is greater than 70%, that classifier is considered fair [44].…”

Section: The Performance Comparison By Using the Rnnmentioning

confidence: 99%

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Alakuş¹,

Türkoğlu²

2021

Turk J Elec Eng & Comp Sci

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Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since, proteins are highly used in smart drug therapies, protein functions and in some case, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, it is needed a computer and artificial intelligence based classification system to save the time, and avoid complexity in protein classification process. In order to designate the protein families with computer-aided systems, protein sequences need to be converted to the numerical representations. In this paper, we provide a novel protein mapping method based on Fibonacci numbers and hashing table (FIBHASH). Each amino acid code is assigned to the Fibonacci numbers based on integer representations respectively. Later, these amino acid codes are inserted a hashing table with the size of 20 to be classified with recurrent neural networks. To determine the performance of the proposed mapping method, we used accuracy, f1-score, recall, precision, and AUC evaluation criteria. In addition, the results of evaluation metrics with other protein mapping techniques including EIIP, hydrophobicity, CPNR, Atchley factors, BLOSUM62, PAM250, binary one-hot encoding, and randomly encoded representations are compared. The proposed method showed a promising result with an accuracy of 92.77%, and 0.98 AUC score.

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“…For example, positron emission tomography (PET), computed tomography (CT), Cancer Antigen 125 (CA125), and Carbohydrate Antigen 19‐9 (CA19‐9) are all used to monitor ovarian cancer recurrence after initial treatment . Several statistical methods for combining multiple biomarkers have been developed for cross sectional studies: including methods based on Fisher's linear discriminant function, receiver operating characteristic (ROC) analysis, and model‐based methods . The common conclusion from the literature is that the diagnostic performance of combined biomarkers for cross sectional studies is better than the performance of a single marker.…”

Section: Introductionmentioning

confidence: 99%

Combining biomarker trajectories to improve diagnostic accuracy in prospective cohort studies with verification bias

Gatsonis

2018

Statistics in Medicine

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In this paper, we develop methods to combine multiple biomarker trajectories into a composite diagnostic marker using functional data analysis (FDA) to achieve better diagnostic accuracy in monitoring disease recurrence in the setting of a prospective cohort study. In such studies, the disease status is usually verified only for patients with a positive test result in any biomarker and is missing in patients with negative test results in all biomarkers. Thus, the test result will affect disease verification, which leads to verification bias if the analysis is restricted only to the verified cases. We treat verification bias as a missing data problem. Under both missing at random (MAR) and missing not at random (MNAR) assumptions, we derive the optimal classification rules using the Neyman‐Pearson lemma based on the composite diagnostic marker. We estimate thresholds adjusted for verification bias to dichotomize patients as test positive or test negative, and we evaluate the diagnostic accuracy using the verification bias corrected area under the ROC curves (AUCs). We evaluate the performance and robustness of the FDA combination approach and assess the consistency of the approach through simulation studies. In addition, we perform a sensitivity analysis of the dependency between the verification process and disease status for the approach under the MNAR assumption. We apply the proposed method on data from the Religious Orders Study and from a non‐small cell lung cancer trial.

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AUC-based biomarker ensemble with an application on gene scores predicting low bone mineral density

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 15 publications

References 21 publications

A classification for complex imbalanced data in disease screening and early diagnosis

A classification for complex imbalanced data in disease screening and early diagnosis

A novel Fibonacci hash method for protein family identification by using recurrent neural networks

Combining biomarker trajectories to improve diagnostic accuracy in prospective cohort studies with verification bias

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