Rank-based Method Research Articles

Proportion estimation in multistage pair ranked set sampling

Abstract Estimating the proportion of individuals having a disease in a given population is a common problem in medicine. This is simply done by drawing a random sample from the target population, and computing the proportion of positive results based on a suitable diagnostic test. In some situations, the number of quantified units is limited because measuring the variable of interest is difficult or expensive. In this setting, one can utilize alternative designs that allow achieving the desired precision level with a smaller sample size. Multistage pair ranked set sampling (MSPRSS) is such a design that can be used instead of simple random sampling. It is a rank-based sampling method that incorporates auxiliary information in order to collect an informative sample. This article deals with the proportion estimation in MSPRSS. Some results about the proposed estimator are proved. A simulation experiment and a real data set in the context of breast cancer are used to demonstrate the finite sample properties of the new estimator.

RC-DETR: Improving DETRs in crowded pedestrian detection via rank-based contrastive learning

The variants of DEtection TRansformer (DETRs) have achieved impressive performance in general object detection. However, they suffer notable performance degradation in scenarios involving crowded pedestrian detection. This decline primarily occurs during the training phase, where DETRs are constrained solely by pedestrian labels. This limitation leads to the production of indistinguishable image features between visually similar pedestrians and background elements, resulting in incorrect detections. To address this issue, this paper introduces a rank-based contrastive learning method, which constructs an additional and specific constraint for each indistinguishable training sample to produce distinguishable image features. Unlike previous methods that rely solely on pedestrian labels to achieve a consistent confidence score, our approach relies on multiple constraints and aims to ensure the correct rank of detection results, with confidence scores of pedestrians consistently surpassing those of background elements. Specifically, we first filter out some training samples that could interfere with our delineation of indistinguishable and distinguishable training samples. Then, based on the confidence score rank, we divide the rest of the training samples into distinguishable positive and negative training samples and indistinguishable positive and negative training samples. Finally, we combine these training samples into multiple positive and negative pairs and utilize these sample pairs to train DETRs via contrastive learning. Our method can be plugged into any DETRs and does not increase any overhead on inference. Extensive experiments on three DETRs show that our method achieves superior performance. Especially on the Crowdhuman dataset, our method achieved the state-of-the-art 38.9% MR.

A rank-based estimation method for mixed effects models in the presence of outlying data

We present an approach to the rank-based estimation of mixed effects models, extending existing methods to random effects structures beyond random intercepts. The estimates obtained from our procedure are insensitive to outlying observations including leverage points, (almost) tuning-parameter free and can be computed very efficiently. Furthermore, the resulting estimates allow for model diagnostics, especially with respect to the identification of outlying observations or groups in the data. The properties of the proposed estimators, in particular, their robustness to different outlier types, are studied by means of simulation studies. The methodology is illustrated with applications to the sleep study data set and to data from accelerated aging experiments on photovoltaic (PV) modules.

Identification of a rank-based radiomic signature with individualized prognostic value for lung adenocarcinoma in a multi-cohort study

ObjectivesRadiomics provides an opportunity to evaluate cancer prognosis noninvasively. However, the susceptibility of the radiomic quantitative features to multicenter effects, leads to the clinical dilemma of the radiomic signatures. This study aimed to develop a radiomic signature to circumvent multicenter effects, achieving the individualized prognostic assessment of lung adenocarcinoma (LUAD). MethodsUsing computed tomography (CT) imaging of 234 stage I–IIIA LUAD patients derived from three public multicenter cohorts, we proposed a rank-based method that utilized the relative rank patterns of quantitative values between radiomic feature pairs within individual patients and established a feature pair signature for LUAD prognosis. We collected a new clinical cohort with 162 LUAD patients for independent validation. ResultsA rank-based radiomic signature, consisting of 12 feature pairs, was developed, and it could determine the mortality risk for an individual according to the rank patterns of 12 feature pairs within the patient’s CT imaging. The prognostic performance of the rank-based signature was effectively validated in the new clinical cohort (log-rank P = 0.0051, C-index = 0.73), whereas other signatures lost their prognostic ability across centers. The novel proposed radiomic nomogram significantly improved the prognostic performance of clinicopathological factors. The further radiogenomic analyses revealed the underlying biological characteristics (e.g., Stemness, Ferroptosis, ’ECM’) reflected by the rank-based radiomic signature. ConclusionsThis multicenter study illustrates the accuracy and stability of the rank-based radiomic signature for LUAD prognosis, and demonstrates a unique advantage of clinical individualized application. The biological characteristics underlying the rank-based radiomic signature would accelerate its clinical application.

PyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R.

Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly when features influence the target in interactive, nonlinear, or nonadditive ways. Currently, some of the most efficient Random Forest methods in terms of computational speed are implemented in Python. However, many biologists use R for genomic data analysis, as R offers a unified platform for performing additional statistical analysis and visualization. Here, we present an R package, pyRforest, which integrates Python scikit-learn "RandomForestClassifier" algorithms into the R environment. pyRforest inherits the efficient memory management and parallelization of Python, and is optimized for classification tasks on large genomic datasets, such as those from RNA-seq. pyRforest offers several additional capabilities, including a novel rank-based permutation method for biomarker identification. This method can be used to estimate and visualize P-values for individual features, allowing the researcher to identify a subset of features for which there is robust statistical evidence of an effect. In addition, pyRforest includes methods for the calculation and visualization of SHapley Additive exPlanations values. Finally, pyRforest includes support for comprehensive downstream analysis for gene ontology and pathway enrichment. pyRforest thus improves the implementation and interpretability of Random Forest models for genomic data analysis by merging the strengths of Python with R. pyRforest can be downloaded at: https://www.github.com/tkolisnik/pyRforest with an associated vignette at https://github.com/tkolisnik/pyRforest/blob/main/vignettes/pyRforest-vignette.pdf.

A best-match approach for gene set analyses in embedding spaces.

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

Improved rank-based recursive feature elimination method based ovarian cancer detection model via customized deep architecture

BackgroundOvarian cancer is often considered the most lethal gynecological cancer because it tends to be diagnosed at an advanced stage, leading to limited treatment options and poorer outcomes. Several factors contribute to the challenges in managing ovarian cancer, namely rapid metastasis, genetic factors, reproductive history, etc. This necessitates the prompt and precise diagnosis of ovarian cancer in order to carry out efficient treatment plans and give patients who are all impacted by OC the care and support they need. MethodsThis CCLSTM model is suggested under four essential stages including preprocessing, feature extraction, feature selection and detection. Initially, the input data is preprocessed using Improved Two-step Data Normalization. Subsequently, features such as statistical, modified entropy, raw features and mutual information are extracted from the normalized data. Next, obtained features undergo the Improved Rank-based Recursive Feature Elimination method (IR-RFE) to select the most suitable features. Finally, the proposed CCLSTM model takes the selected features as input and provides a final detection outcome. ResultsFurthermore, the performance of the proposed CCLSTM technique is examined through a thorough assessment using diverse analyses Additionally, the CCLSTM schemes show a sensitivity value of 0.948, whereas the sensitivity ratings for ALO-LSTM + ALOCNN, Bi-GRU, LSTM, RNN, KNN, CNN, and DCNN are 0.808, 0.893, 0.829, 0.851, 0.765, 0.872, and 0.893, respectively. ConclusionIn the end, the development of CNN and the addition of LSTM technique have produced an ovarian cancer detection technique that is more accurate and consistent compared to other existing strategies.

Data Collector Selection Ranking-Based Method for Collaborative Multi-Tasks in Ubiquitous Environments

Data Collector Selection Ranking-Based Method for Collaborative Multi-Tasks in Ubiquitous Environments

Improving service accessibility and equity for sustainable development goals without newly facilities by rural settlement reconstruction

Ensuring the provision of accessible, affordable, and high-quality public services to all individuals aligns with one of the paramount aims of the United Nations’ Sustainable Development Goals (SDGs). In the face of escalating urbanization and a dwindling rural populace in China, reconstructing rural settlements to enhance public service accessibility has become a fundamental strategy for achieving the SDGs in rural areas. However, few studies have examined the optimal methods for rural settlement reconstruction that ensure accessible and equitable public services while considering multiple existing facilities and service provisions. This paper focuses on rural settlement reconstruction in the context of the SDGs, employing an inverted MCLP-CC (maximal coverage location problem for complementary coverage) model to identify optimal rural settlements and a rank-based method for their relocation. Conducted in Changyuan, a county-level city in Henan Province, China, this study observed significant enhancements in both accessibility and equity following rural settlement reconstruction by utilizing the MH3SFCA (modified Huff 3-step floating catchment area) and the spatial Lorenz curve method. Remarkably, these improvements were achieved without the addition of new facilities, with the accessibility increasing by 44.21%, 4.97%, and 3.11%; Gini coefficients decreasing by 19.53%, 1.64%, and 3.18%; Ricci-Schutz coefficients decreasing by 21.09%, 2.09%, and 4.33% for educational, medical, and cultural and sports facilities, respectively. It indicated that rural settlement reconstruction can bolster the accessibility and equity of public services by leveraging existing facilities. This paper provides a new framework for stakeholders to better reconstruct rural settlements and promote sustainable development in rural areas in China.

A rank-based adaptive independence test for high-dimensional data

There are lots of methods for complete independence test for high-dimensional random vector. However, it is difficult for practitioners to choose a powerful test because the true alternative hypothesis is unknown. Combining the L 2-type with the L ∞ -type test statistic, we propose a rank-based test method. From a technical point of view, the proposed test is distribution-free and consequently the corresponding critical values or p-values can be obtained by Monte Carlo methods. Compared with permutation or bootstrap test methods, the proposed test method saves calculation cost. Simulation results show that the resulting method has excellent performance with finite sample size. We also provide a real data application to demonstrate the practicality and effectiveness of the proposed test method.

Common variation in a long non-coding RNA gene modulates variation of circulating TGF-β2 levels in metastatic colorectal cancer patients (Alliance)

BackgroundHerein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC). The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings.ResultsThe final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-β2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73.ConclusionsThis study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-β2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations.

Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.

REFERENCE INTERVAL OF PLACENTAL GROWTH FACTOR IN NON-PREGNANT AND PREGNANT FEMALES

Objective: To determine the reference interval of placental growth factor in healthy non-pregnant and pregnant females. Material and Methods: This cross-sectional study was conducted at Chughtai Institute of Pathology Chemical pathology department from January 2023-May 2023. Reference individuals were selected by purposive, non-probability sampling technique from 130 diseased free non-pregnant and 130 disease free pregnant females. Informed consent was obtained from females of reproductive age group who fulfilled the inclusion criteria. Levels for PGF was measured by a fully automated immunoassay analyser Elecsys system Cobas e601. Data was analyzed by using SPSS21. Kolmogorov-Smirnov test was applied. P value &lt;0.05 was considered significant. The 2.5th and 97.5th percentiles were calculated at 90% CI by using the formula 0.025x(n+1) at rank number 1 and 0.975x(n+1) at rank number 7. Results: Reference intervals were calculated by Rank-based method. Data values were arranged and rank numbers were allocated. Reference interval of PGF in non-pregnant and pregnant females were determine on the basis of 2.5th and 97.5th percentiles was 3.8 to 12.7 pg/mL and 46.43 to 1148 pg/mL respectively. Conclusion: Current study findings add on contribution in comparison between reference interval of PGF in healthy non-pregnant and pregnant females. It will subsequently help the gynaecologist, clinicians, pathologist to interpret the results in order to decrease the feto/maternal complications related to pre-eclampsia. PGF levels can be cost effective by reducing unnecessary hospitalization and investigations in females at minimal risk of pre-eclampsia. Keywords: Reference interval, Placental growth factor, Pre-eclampsia.

Identifying Popular Products at an Early Stage of Sales Season for Apparel Industry

The early phase of launching a new apparel product is critical for gaining insights of its performance and classifying it into different categories such as fast selling, average selling, and slow selling. We propose a new ranking-based method to identify the product popularity that predicts regional and national rankings of products based on sales data at an early stage of a sales season. Our method enables companies to efficiently identify popular products within a remarkably short span of two to four weeks.

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet to be established and different studies on uncertainties generally uses different metrics to evaluate them. We compare three of the most popular validation metrics (Spearman’s rank correlation coefficient, the negative log likelihood (NLL) and the miscalibration area) to the error-based calibration introduced by Levi et al. (Sensors2022, 22, 5540). Importantly, metrics such as the negative log likelihood (NLL) and Spearman’s rank correlation coefficient bear little information in themselves. We therefore introduce reference values obtained through errors simulated directly from the uncertainty distribution. The different metrics target different properties and we show how to interpret them, but we generally find the best overall validation to be done based on the error-based calibration plot introduced by Levi et al. Finally, we illustrate the sensitivity of ranking-based methods (e.g. Spearman’s rank correlation coefficient) towards test set design by using the same toy model ferent test sets and obtaining vastly different metrics (0.05 vs. 0.65).

Application of Recurrent Neural Networks Based on Attentional Mechanisms in Classification Error Correction in English Teaching

Abstract The way teachers correct errors in English teaching can cause problems such as psychological pressure, and deep learning technology offers the possibility of automatic error correction. In this paper, the final states of left and right texts are computed by constructing two attention mechanisms, target word-independent and related, and the merged obtained vectors are inputted into the RNN model for grammatical error recognition. In collocation error recognition, a Rank-based word candidate set ranking method is added, and error correction for verb usage is semantically encoded using RNN. The study was analyzed and tested in terms of grammar, collocation, and verbs. The ATT-RNN model accuracy is 3.62 percentage points higher than CAMB, and the difference in recall and F 0.5-value is not more than 0.5 percentage points, which indicates that the algorithmic model has some research value.

Ranking-Based Color Constancy With Limited Training Samples.

Computational color constancy is an important component of Image Signal Processors (ISP) for white balancing in many imaging devices. Recently, deep convolutional neural networks (CNN) have been introduced for color constancy. They achieve prominent performance improvements comparing with those statistics or shallow learning-based methods. However, the need for a large number of training samples, a high computational cost and a huge model size make CNN-based methods unsuitable for deployment on low-resource ISPs for real-time applications. In order to overcome these limitations and to achieve comparable performance to CNN-based methods, an efficient method is defined for selecting the optimal simple statistics-based method (SM) for each image. To this end, we propose a novel ranking-based color constancy method (RCC) that formulates the selection of the optimal SM method as a label ranking problem. RCC designs a specific ranking loss function, and uses a low rank constraint to control the model complexity and a grouped sparse constraint for feature selection. Finally, we apply the RCC model to predict the order of the candidate SM methods for a test image, and then estimate its illumination using the predicted optimal SM method (or fusing the results estimated by the top k SM methods). Comprehensive experiment results show that the proposed RCC outperforms nearly all the shallow learning-based methods and achieves comparable performance to (sometimes even better performance than) deep CNN-based methods with only 1/2000 of the model size and training time. RCC also shows good robustness to limited training samples and good generalization crossing cameras. Furthermore, to remove the dependence on the ground truth illumination, we extend RCC to obtain a novel ranking-based method without ground truth illumination (RCC_NO) that learns the ranking model using simple partial binary preference annotations provided by untrained annotators rather than experts. RCC_NO also achieves better performance than the SM methods and most shallow learning-based methods with low costs of sample collection and illumination measurement.

A Multi-stage framework for COVID-19 detection and severity assessment from chest radiography images using advanced fuzzy ensemble technique

Chest radiography (CXR) is a popular imaging modality for screening lung abnormalities. It plays a vital role in monitoring disease progression, allocating limited medical resources effectively, and planning treatment in clinical practice. However, manual CXR image screening is time-consuming and susceptible to human errors. Therefore, computer-aided diagnosis (CAD) systems can be an additional decision-making tool for radiologists. Although prior research had remarkable success in COVID-19 classification tasks, it is still difficult to accurately localize the infection and quantify its severity, which restricts the generalizability and interpretability of CAD-based systems. This work proposes an end-to-end multi-stage architecture for COVID-19 classification, infection region identification, and disease severity assessment. The first stage of the proposed architecture identifies the relevant lung regions by eliminating the irrelevant portions. The second stage discriminates COVID-19 from pneumonia and normal images. In this study, instead of a single pre-trained model, we used three models and a fuzzy rank-based ensemble method to combine the results. Unlike the simple ensemble techniques, the proposed approach generates the final predictions for the test samples by evaluating the confidence in the predictions of the base models. In the final stage of the network, the infection regions are identified, and the amount of infection is calculated when the image is classified as COVID-19. Subsequently, a severity score ranging from 0 to 3 is assigned based on the level of infection severity. Based on the predicted scores, COVID-19 images are categorized into four severity levels: mild, moderate, severe, and critical. The final ensemble segmentation model exceeds all the individual segmentation models with an IoU score of 98.99% and a dice score of 99.49%. Additionally, the infection segmentation model also outperforms all other segmentation models individually, achieving a dice score of 96.75% and an IoU score of 93.71%. Our proposed segmentation-based classification model achieves a maximum accuracy of 98.05% with an F1-score of 97.76% and outperforms five state-of-the-art (SOTA) CNN models and five existing ensemble techniques in the literature. An external dataset also validated the model, and the results showed an accuracy of 97.63% and an F1-score of 97.22%.

Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data.

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.

Imaging point diffractors using a low-rank approximation method

Diffraction separation plays a critical role in obtaining high-resolution images of subsurface diffractors by eliminating the diffractions’ contribution from reflections. It is usually based on the difference in spatial coherency between seismic reflection and diffraction energy in the time domain. Methods based on local slope differences, e.g., plane-wave destruction (PWD) method, tend to separate point and line diffractors simultaneously. This paper applies a rank-based diffraction separation method, known as the localized rank-reduction (LRR) method, to distinguish point diffractors in poststack data sets from line diffractors, thereby obtaining a higher resolution when characterizing point discontinuities such as karst caves. To make the LRR method more adaptive, a novel rank determination method based on a newly defined accumulated singular value ratio sequence is developed. The effectiveness of the proposed method is proved via a variety of 2D/3D synthetic and real data examples and a detailed discussion of its practical potential. Despite a larger computational cost compared to the PWD method, the proposed method helps interpretation by enabling us to distinguish between line and point diffractors.