Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma

Bhalla, Sherry; Kaur, Harpreet; Kaur, Ramneek; Sharma, Suresh; Raghava, Gajendra P. S.

doi:10.1371/journal.pone.0231629

Cited by 14 publications

(9 citation statements)

References 70 publications

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“…This was demonstrated in the numeric example in Section 2 for which the within-class F 1 values are 0.308, 0.927, and 0.833, and miF 1 , maF 1 , and

are 0.870, 0.689, and 0.691, respectively. Reporting multiple within-class F 1 scores may be an option as done in [ 18 ] and [ 19 ]; however, an aggregate measure is useful in evaluating an overall performance of a classifier across classes. Another limitation with F 1 scores is that they do not take into consideration the true negative rate, and they may not be an appropriate measure when true negatives are important.…”

Section: Discussionmentioning

confidence: 99%

Confidence interval for micro-averaged F1 and macro-averaged F1 scores

et al. 2021

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A binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor. In computer science, a classifier is usually evaluated with precision (positive predictive value) and recall (sensitivity). As a single summary measure of a classifier’s performance, F1 score, defined as the harmonic mean of precision and recall, is widely used in the context of information retrieval and information extraction evaluation since it possesses favorable characteristics, especially when the prevalence is low. Some statistical methods for inference have been developed for the F1 score in binary classification problems; however, they have not been extended to the problem of multi-class classification. There are three types of F1 scores, and statistical properties of these F1 scores have hardly ever been discussed. We propose methods based on the large sample multivariate central limit theorem for estimating F1 scores with confidence intervals.

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“…This was demonstrated in the numeric example in Section 2 for which the within-class F 1 values are 0.308, 0.927, and 0.833, and miF 1 , maF 1 , and

Section: Discussionmentioning

confidence: 99%

Confidence interval for micro-averaged F1 and macro-averaged F1 scores

et al. 2021

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“…However, the performance on the independent dataset remains dismal. Besides, past literature indicates that accurate tumor stage prediction is itself a challenging task [45,52,67,84,85]. SUZ12 encodes a protein subunit of transcription Polycomb Repressive Complex (PRC2) that represses target proteins.…”

Section: Discussionmentioning

confidence: 99%

“…To further reduce the feature dimension from these significant features, we applied SVC-L1, ANOVA_F (or f_classif), principal component analysis (PCA), and autoencoder-based deep learning techniques. In our study, we used the SVC-L1 feature selection method, which was previously used for classification purposes in different malignancies conditions [45,67].…”

Section: Feature Selection and Extraction Techniquesmentioning

confidence: 99%

Integrative multi-omics approach for stratification of tumor recurrence risk groups of Hepatocellular Carcinoma patients

Kaur

Lathwal

Raghava

2021

Preprint

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Postoperative tumor recurrence is one of the major concerns associated with the poor prognosis of HCC patients. There is yet to elucidate a standard surveillance system for HCC recurrence risk owing to complexity of this malignancy. Generation of multi-omics data from patients facilitate the identification of robust signatures for various diseases. Thus, the current study is an attempt to develop the prognostic models employing multi-omics data to significantly (p-value <0.05) stratify the recurrence high-risk (median Recurrence Free Survival time (RFS) =<12 months) and low-risk groups (median RFS >12 months). First, we identified key 90RNA, 50miRNA and 50 methylation features and developed prognostic models; attained reasonable performance (C-Index >0.70, HR >2.5), on training and validation datasets. Subsequently, we developed a prognostic (PI) model by integrating the four multi-omics features (SUZ12, hsa-mir-3936, cg18465072, and cg22852503), that are biologically inter-linked with each other. This model achieved reasonable performance on training and validation dataset, i.e. C-Index 0.72, HR of 2.37 (1.61 - 3.50), p-value of 6.72E-06, Brier score 0.19 on training dataset, and C-Index 0.72 (95% CI: 0.63 - 0.80), HR of 2.37 (95% CI: 1.61 - 3.50), p-value of 0.015, Brier score 0.19 on validation dataset. Eventually, Drugbank data was investigated to elucidate therapeutic potential of these signatures. We have identified nine potential drugs against three genes (CA9, IL1A, KCNJ15) that are positively correlated with the tumor recurrence. We anticipate these results from our study will help researchers and clinicians to improve the HCC recurrence surveillance, eventually outcome of patients.

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“…The MLE of maF 1 and maF 2 can be obtained by substituting p a.a , p .aa , p a.. , p .a. and p ..a by their MLE's in (7) and (8). Again by the delta-method and multivariate central limit theorem (Appendix A.3), we have the Wald statistic for testing…”

Section: Test Statistic For Comparing Two Mafsmentioning

confidence: 99%

Hypothesis testing procedure for binary and multi‐class F₁‐scores in the paired design

Takahashi,

Yamamoto,

Kuchiba

et al. 2023

Statistics in Medicine

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In modern medicine, medical tests are used for various purposes including diagnosis, disease screening, prognosis, and risk prediction. To quantify the performance of the binary medical test, we often use sensitivity, specificity, and negative and positive predictive values as measures. Additionally, the ‐score, which is defined as the harmonic mean of precision (positive predictive value) and recall (sensitivity), has come to be used in the medical field due to its favorable characteristics. The ‐score has been extended for multi‐class classification, and two types of ‐scores have been proposed for multi‐class classification: a micro‐averaged ‐score and a macro‐averaged ‐score. The micro‐averaged ‐score pools per‐sample classifications across classes and then calculates the overall ‐score, whereas the macro‐averaged ‐score computes an arithmetic mean of the ‐scores for each class. Additionally, Sokolova and Lapalme gave an alternative definition of the macro‐averaged ‐score as the harmonic mean of the arithmetic means of the precision and recall over classes. Although some statistical methods of inference for binary and multi‐class ‐scores have been proposed, the methodology development of hypothesis testing procedure for them has not been fully progressing yet. Therefore, we aim to develop hypothesis testing procedure for comparing two ‐scores in paired study design based on the large sample multivariate central limit theorem.

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Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma

Cited by 14 publications

References 70 publications

Confidence interval for micro-averaged F1 and macro-averaged F1 scores

Confidence interval for micro-averaged F1 and macro-averaged F1 scores

Integrative multi-omics approach for stratification of tumor recurrence risk groups of Hepatocellular Carcinoma patients

Hypothesis testing procedure for binary and multi‐class F₁‐scores in the paired design

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Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma

Cited by 14 publications

References 70 publications

Confidence interval for micro-averaged F1 and macro-averaged F1 scores

Confidence interval for micro-averaged F1 and macro-averaged F1 scores

Integrative multi-omics approach for stratification of tumor recurrence risk groups of Hepatocellular Carcinoma patients

Hypothesis testing procedure for binary and multi‐class F1‐scores in the paired design

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Product

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Hypothesis testing procedure for binary and multi‐class F₁‐scores in the paired design