Rank-based Method Research Articles

Rank-based camera spectral sensitivity estimation

In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab-a difficult and lengthy procedure-or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have "raw mode." Experiments validate our method.

On the Analytical Solution of Rank Problem in the Convolutional Code Identification Context

The rank-based method is very effective to identify the convolutional code parameters in a non-cooperative context. This method cuts the received sequence up into vectors of length $l$ to form the rows of matrix $C^{(l)}$ . It is well known that there is a relationship between the code parameters and the rank behavior of $C^{(l)}$ . However, finding the general form of this relation is a challenging open problem. In this letter, we formulate and solve this problem for rate $\frac{k}{n}$ convolutional codes. Our analytical solution show that the rank behavior is highly dependent on the dual code constraint lengths. Moreover, it is proved that the applied experimental rank relation in prior works is true only for rate $\frac{n-1}{n}$ codes.

Rank repeated measures analysis of covariance

When analyzing a response variable at the presence of both factors and covariates, with potentially correlated responses and violated assumptions of the normal residual or the linear relationship between the response and the covariates, rank-based tests can be an option for inferential procedures instead of the parametric repeated measures analysis of covariance (ANCOVA) models. This article derives a rank-based method for multi-way ANCOVA models with correlated responses. The generalized estimating equations (GEE) technique is employed to construct the proposed rank tests. Asymptotic properties of the proposed tests are derived. Simulation studies confirmed the performance of the proposed tests.

Estimation of sparse directed acyclic graphs for multivariate counts data.

The next-generation sequencing data, called high-throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this article provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study.

An overview of the estimation of large covariance and precision matrices

Estimating large covariance and precision matrices are fundamental in modern multivariate analysis. The problems arise from statistical analysis of large panel economics and finance data. The covariance matrix reveals marginal correlations between variables, while the precision matrix encodes conditional correlations between pairs of variables given the remaining variables. In this paper, we provide a selective review of several recent developments on estimating large covariance and precision matrices. We focus on two general approaches: rank based method and factor model based method. Theories and applications of both approaches are presented. These methods are expected to be widely applicable to analysis of economic and financial data.

Rank-Based Image Watermarking Method With High Embedding Capacity and Robustness

This paper presents a novel rank-based method for image watermarking. In the watermark embedding process, the host image is divided into blocks, followed by the 2-D discrete cosine transform (DCT). For each image block, a secret key is employed to randomly select a set of DCT coefficients suitable for watermark embedding. Watermark bits are inserted into an image block by modifying the set of DCT coefficients using a rank-based embedding rule. In the watermark detection process, the corresponding detection matrices are formed from the received image using the secret key. Afterward, the watermark bits are extracted by checking the ranks of the detection matrices. Since the proposed watermarking method only uses two DCT coefficients to hide one watermark bit, it can achieve very high embedding capacity. Moreover, our method is free of host signal interference. This desired feature and the usage of an error buffer in watermark embedding result in high robustness against attacks. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.

Test-retest reliability at the item level and total score level of the Norwegian version of the Spinal Cord Injury Falls Concern Scale (SCI-FCS)

Objectives: Translation of the Spinal Cord Injury Falls Concern Scale (SCI-FCS), and investigation of test-retest reliability on item-level and total-score-level.Design: Translation, adaptation and test-retest study.Setting: A specialized rehabilitation setting in Norway.Participants: Fifty-four wheelchair users with a spinal cord injury. The median age of the cohort was 49 years, and the median number of years after injury was 13.Interventions/measurements: The SCI-FCS was translated and back-translated according to guidelines. Individuals answered the SCI-FCS twice over the course of one week. We investigated item-level test-retest reliability using Svensson's rank-based statistical method for disagreement analysis of paired ordinal data. For relative reliability, we analyzed the total-score-level test-retest reliability with intraclass correlation coefficients (ICC2.1), the standard error of measurement (SEM), and the smallest detectable change (SDC) for absolute reliability/measurement-error assessment and Cronbach's alpha for internal consistency.Results: All items showed satisfactory percentage agreement (≥69%) between test and retest. There were small but non-negligible systematic disagreements among three items; we recovered an 11–13% higher chance for a lower second score. There was no disagreement due to random variance. The test-retest agreement (ICC2.1) was excellent (0.83). The SEM was 2.6 (12%), and the SDC was 7.1 (32%). The Cronbach's alpha was high (0.88).Conclusion: The Norwegian SCI-FCS is highly reliable for wheelchair users with chronic spinal cord injuries.

위키피디아 링크를 이용한 랭크 기반 개념 계층구조의 자동 구축

In general, we have utilized the hierarchical concept tree as a crucial data structure for indexing huge amount of textual data. This paper proposes a generality rank-based method that can automatically develop hierarchical concept structures with the Wikipedia data. The goal of the method is to regard each of Wikipedia articles as a concept and to generate hierarchical relationships among concepts. In order to estimate the generality of concepts, we have devised a special ranking function that mainly uses the number of hyperlinks among Wikipedia articles. The ranking function is effectively used for computing the probabilistic subsumption among concepts, which allows to generate relatively more stable hierarchical structures. Eventually, a set of concept pairs with hierarchical relationship is visualized as a DAG (directed acyclic graph). Through the empirical analysis using the concept hierarchy of Open Directory Project, we proved that the proposed method outperforms a representative baseline method and it can automatically extract concept hierarchies with high accuracy.

Two hybrid wrapper-filter feature selection algorithms applied to high-dimensional microarray experiments

Microarray experiments generally deal with complex and high-dimensional samples, and in addition, the number of samples is much smaller than their dimensions. Both issues can be alleviated by using a feature selection (FS) method. In this paper two new, simple, and efficient hybrid FS algorithms, called respectively BDE-XRank and BDE-XRankf, are presented. Both algorithms combine a wrapper FS method based on a Binary Differential Evolution (BDE) algorithm with a rank-based filter FS method. Besides, they generate the initial population with solutions involving only a small number of features. Some initial solutions are built considering only the most relevant features regarding the filter method, and the remaining ones include only random features (to promote diversity). In the BDE-XRankf, a new fitness function, in which the score value of a solution is influenced by the frequency of the features in the current population, is incorporated in the algorithm. The robustness of BDE-XRank and BDE-XRankf is shown by using four Machine Learning (ML) algorithms (NB, SVM, C4.5, and kNN). Six high-dimensional well-known data sets of microarray experiments are used to carry out an extensive experimental study based on statistical tests. This experimental analysis shows the robustness as well as the ability of both proposals to obtain highly accurate solutions at the earlier stages of BDE evolutionary process. Finally, BDE-XRank and BDE-XRankf are also compared against the results of nine state-of-the-art algorithms to highlight its competitiveness and the ability to successfully reduce the original feature set size by more than 99%.

Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer.

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.

Global-Scale Location Prediction for Social Images Using Geo-Visual Ranking

We propose an automatic method that addresses the challenge of predicting the geo-location of social images using only the visual content of those images. Our method is able to generate a geo-location prediction for an image globally . In this respect, it contrasts with other existing approaches, specifically with those that generate predictions restricted to specific cities, landmarks, or an otherwise pre-defined set of locations. The essence and the main novelty of our ranking-based method is that for a given query image a geo-location is recommended based on the evidence collected from images that are not only geographically close to this geo-location, but also have sufficient visual similarity to the query image within the considered image collection. Our method is evaluated experimentally on a public dataset of 8.8 million geo-tagged images from Flickr, released by the MediaEval 2013 evaluation benchmark. Experiments show that the proposed method delivers a substantial performance improvement compared to the existing related approaches, particularly for queries with high numbers of neighbors . In addition, a detailed analysis of the method's performance reveals the impact of different visual feature extraction and image matching strategies, as well as the densities and types of images found at different locations, on the prediction accuracy.

Identification of chemical modulators of the constitutive activated receptor (CAR) in a gene expression compendium.

The nuclear receptor family member constitutive activated receptor (CAR) is activated by structurally diverse drugs and environmentally-relevant chemicals leading to transcriptional regulation of genes involved in xenobiotic metabolism and transport. Chronic activation of CAR increases liver cancer incidence in rodents, whereas suppression of CAR can lead to steatosis and insulin insensitivity. Here, analytical methods were developed to screen for chemical treatments in a gene expression compendium that lead to alteration of CAR activity. A gene expression biomarker signature of 83 CAR-dependent genes was identified using microarray profiles from the livers of wild-type and CAR-null mice after exposure to three structurally-diverse CAR activators (CITCO, phenobarbital, TCPOBOP). A rank-based algorithm (Running Fisher’s algorithm (p-value ≤ 10-4)) was used to evaluate the similarity between the CAR biomarker signature and a test set of 28 and 32 comparisons positive or negative, respectively, for CAR activation; the test resulted in a balanced accuracy of 97%. The biomarker signature was used to identify chemicals that activate or suppress CAR in an annotated mouse liver/primary hepatocyte gene expression database of ~1850 comparisons. CAR was activated by 1) activators of the aryl hydrocarbon receptor (AhR) in wild-type but not AhR-null mice, 2) pregnane X receptor (PXR) activators in wild-type and to lesser extents in PXR-null mice, and 3) activators of PPARα in wild-type and PPARα-null mice. CAR was consistently activated by five conazole fungicides and four perfluorinated compounds. Comparison of effects in wild-type and CAR-null mice showed that the fungicide propiconazole increased liver weight and hepatocyte proliferation in a CAR-dependent manner, whereas the perfluorinated compound perfluorooctanoic acid (PFOA) increased these endpoints in a CAR-independent manner. A number of compounds suppressed CAR coincident with increases in markers of inflammation including acetaminophen, concanavalin A, lipopolysaccharide, and 300 nm silica particles. In conclusion, we have shown that a CAR biomarker signature coupled with a rank-based similarity method accurately predicts CAR activation. This analytical approach, when applied to a gene expression compendium, increased the universe of known chemicals that directly or indirectly activate CAR, highlighting the promiscuous nature of CAR activation and signaling through activation of other xenobiotic-activated receptors.

Scoring Situational Judgement Tests: Evolving approaches

While it is well established that Situational Judgement Tests (SJTs) can be valid predictors of workplace performance (e.g. McDaniel et al., 2007), research on the effectiveness of different scoring methods is still evolving (e.g. St-Sauveur et al., 2014; Hughes, 2014). This article uses practical examples to illustrate how the scoring of ranking-based methods could be enhanced.

Application of the rank-based method to DNA methylation for cancer diagnosis.

Detecting aberrant DNA methylation as diagnostic or prognostic biomarkers for cancer has been a topic of considerable interest recently. However, current classifiers based on absolute methylation values detected from a cohort of samples are typically difficult to be transferable to other cohorts of samples. Here, focusing on relative methylation levels, we employed a modified rank-based method to extract reversal pairs of CpG sites whose relative methylation level orderings differ between disease samples and normal controls for cancer diagnosis. The reversal pairs identified for five cancer types respectively show excellent prediction performance with the accuracy above 95%. Furthermore, when evaluating the reversal pairs identified for one cancer type in an independent cohorts of samples, we found that they could distinguish different subtypes of this cancer or different malignant stages including early stage of this cancer from normal controls. The identified reversal pairs also appear to be specific to cancer type. In conclusion, the reversal pairs detected by the rank-based method could be used for accurate cancer diagnosis, which are transferable to independent cohorts of samples.

A new topology optimization scheme for nonlinear structures

A new topology optimization algorithm based on artificial bee colony algorithm (ABCA) was developed and applied to geometrically nonlinear structures. A finite element method and the Newton-Raphson technique were adopted for the nonlinear topology optimization. The distribution of material is expressed by the density of each element and a filter scheme was implemented to prevent a checkerboard pattern in the optimized layouts. In the application of ABCA for long structures or structures with small volume constraints, optimized topologies may be obtained differently for the same problem at each trial. The calculation speed is also very slow since topology optimization based on the roulette-wheel method requires many finite element analyses. To improve the calculation speed and stability of ABCA, a rank-based method was used. By optimizing several examples, it was verified that the developed topology scheme based on ABCA is very effective and applicable in geometrically nonlinear topology optimization problems.

Annotation of long non-coding RNAs expressed in Collaborative Cross founder mice in response to respiratory virus infection reveals a new class of interferon-stimulated transcripts

The outcome of respiratory virus infection is determined by a complex interplay of viral and host factors. Some potentially important host factors for the antiviral response, whose functions remain largely unexplored, are long non-coding RNAs (lncRNAs). Here we systematically inferred the regulatory functions of host lncRNAs in response to influenza A virus and severe acute respiratory syndrome coronavirus (SARS-CoV) based on their similarity in expression with genes of known function. We performed total RNA-Seq on viral-infected lungs from eight mouse strains, yielding a large data set of transcriptional responses. Overall 5,329 lncRNAs were differentially expressed after infection. Most of the lncRNAs were co-expressed with coding genes in modules enriched in genes associated with lung homeostasis pathways or immune response processes. Each lncRNA was further individually annotated using a rank-based method, enabling us to associate 5,295 lncRNAs to at least one gene set and to predict their potential cis effects. We validated the lncRNAs predicted to be interferon-stimulated by profiling mouse responses after interferon-α treatment. Altogether, these results provide a broad categorization of potential lncRNA functions and identify subsets of lncRNAs with likely key roles in respiratory virus pathogenesis. These data are fully accessible through the MOuse NOn-Code Lung interactive database (MONOCLdb).

Pathway-Centric Integrative Analysis Identifies RRM2 as a Prognostic Marker in Breast Cancer Associated with Poor Survival and Tamoxifen Resistance

Breast cancer (BCa) molecular subtypes include luminal A, luminal B, normal-like, HER-2–enriched, and basal-like tumors, among which luminal B and basal-like cancers are highly aggressive. Biochemical pathways associated with patient survival or treatment response in these more aggressive subtypes are not well understood. With the limited availability of pathologically verified clinical specimens, cell line models are routinely used for pathway-centric studies. We measured the metabolome of luminal and basal-like BCa cell lines using mass spectrometry, linked metabolites to biochemical pathways using Gene Set Analysis, and developed a novel rank-based method to select pathways on the basis of their enrichment in patient-derived omics data sets and prognostic relevance. Key mediators of the pathway were then characterized for their role in disease progression. Pyrimidine metabolism was altered in luminal versus basal BCa, whereas the combined expression of its associated genes or expression of one key gene, ribonucleotide reductase subunit M2 (RRM2) alone, associated significantly with decreased survival across all BCa subtypes, as well as in luminal patients resistant to tamoxifen. Increased RRM2 expression in tamoxifen-resistant patients was verified using tissue microarrays, whereas the metabolic products of RRM2 were higher in tamoxifen-resistant cells and in xenograft tumors. Both genetic and pharmacological inhibition of this key enzyme in tamoxifen-resistant cells significantly decreased proliferation, reduced expression of cell cycle genes, and sensitized the cells to tamoxifen treatment. Our study suggests for evaluating RRM2-associated metabolites as noninvasive markers for tamoxifen resistance and its pharmacological inhibition as a novel approach to overcome tamoxifen resistance in BCa.

Identification of novel loci for bipolar I disorder in a multi-stage genome-wide association study

Identification of genetic variants that influence bipolar I disorder (BPD-I) through genome-wide association (GWA) studies is limited in Asian populations. The current study aimed to identify novel common variants for BPD-I in an ethnically homogeneous Taiwanese sample using a multi-stage GWA study design. At the discovery stage, 200 BPD-I patients and 200 controls that combined to form 16 pools were genotyped with 1 million markers. Utilizing a newly developed rank-based method, top-ranked markers were selected. After validation with individual genotyping, a fine-mapping association study was conducted to identify associated loci using 240 patients and 240 controls. At the last stage, independent samples were collected (351 cases and 341 controls) for replication. Among the top-ranked markers from the discovery stage, eight genes and 15 individual SNPs were evaluated in the fine-mapping stage. At this stage, rs7619173, which is not in a gene coding region, showed the most significant association (P = 2 ∗ 10(-5)) with BPD-I. Four genes had empirical P-values<0.05, including KCNH7 (P = 0.0047), MYST4 (P = 0.0047), NRXN3 (P = 0.0095), and SEMA3D (P = 0.037). For markers genotyped in replication samples, rs7619173 exhibited a significant association (P(combined) = 2 ∗ 10(-4)) after multiple testing correction, while markers rs11001178 (MYST4) and rs2217887 (NRXN3) showed weak associations (P(combined) = 0.02) with BPD-I. A multi-stage GWA design has the potential to uncover the underlying pathogenesis of a complex trait. Findings in the present study highlight three loci that warrant further investigation for bipolar.

Intra-rater and inter-rater reliability at the item level of the Action Research Arm Test for patients with stroke.

To examine the intra- and inter-rater reliability of the Action Research Arm Test (ARAT) at the item level after stroke. An intra-rater and inter-rater reliability study. Thirty-five participants (median age 62 years, median time post-stroke 22 months) with impaired upper extremity function after stroke were included in the study. Two physiotherapists simultaneously, but independently, assessed the participants' performance in all 19 items of the ARAT twice in 1 day. A rank-based statistical method for paired ordinal data, including calculation of percentage agreement (PA), systematic disagreements (relative position (RP), relative concentration (RC)) and individual variability (relative rank variance (RV)) was used. Satisfactory intra-rater and inter-rater agreement was noted for all items except item 19, which was just below satisfactory level. Within and between raters, small but non-negligible systematic disagreements were found for items 11, 14 and 19 and for items 1, 4, 17 and 19, respectively. There was no disagreement due to random variance within or between raters. The ARAT is a highly reliable observational rating scale at the item level after stroke. Awareness regarding the small systematic disagreements demonstrated in some items is, however, recommended when using ARAT.

A simulation study on the influence of ties on uniform scores test for circular data

Uniform scores test is a rank-based method that tests the homogeneity of k-populations in circular data problems. The influence of ties on the uniform scores test has been emphasized by several authors in several articles and books. Moreover, it is suggested that the uniform scores test should be used with caution if ties are present in the data. This paper investigates the influence of ties on the uniform scores test by computing the power of the test using average, randomization, permutation, minimum, and maximum methods to break ties. Monte Carlo simulation is performed to compute the power of the test under several scenarios such as having 5% or 10% of ties and tie group structures in the data. The simulation study shows no significant difference among the methods under the existence of ties but the test loses its power when there are many ties or complicated group structures. Thus, randomization or average methods are equally powerful to break ties when applying uniform scores test. Also, it can be concluded that k-sample uniform scores test can be used safely without sacrificing the power if there are only less than 5% of ties or at most two groups of a few ties.