Group Screening Method Research Articles

Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method’s effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening.

Group Feature Screening Based on Information Gain Ratio for Ultrahigh‐Dimensional Data

Most model‐free feature screening approaches focus on the ‐individual predictor; therefore, they are not able to incorporate structured predictors like grouped variables. In this article, we propose a group screening procedure via the information gain ratio for a classification model, which is a direct extension of the original sure independence screening procedure and also model‐free. The proposed method yields a better screening performance and classification accuracy. It is demonstrated that the proposed group screening method possesses the sure screening property and ranking consistency properties under certain regularity conditions. Through simulation studies and real‐world data analysis, we demonstrate the proposed method with the finite sample performance.

Projection quantile correlation and its use in high-dimensional grouped variable screening

In this paper, we propose a new measure, called Projection Quantile Correlation (PQC), to detect quantile dependence between a response and multivariate predictors at a given quantile level. The PQC measure is free of tuning parameters, robust against heavy tailed distributions and outliers, and does not require moment conditions on the predictors. We obtain theoretical properties of the PQC and its empirical counterpart. We then use the measure to select the grouped predictors that contribute to the conditional quantile of the response for high-dimensional data with group structures. We also establish sure independent screening properties for the group screening method. We illustrate the finite sample performance of the proposed method through simulations and an application to a dataset.

Group screening for ultra-high-dimensional feature under linear model

Ultra-high-dimensional data with grouping structures arise naturally in many contemporary statistical problems, such as gene-wide association studies and the multi-factor analysis-of-variance (ANOVA). To address this issue, we proposed a group screening method to do variables selection on groups of variables in linear models. This group screening method is based on a working independence, and sure screening property is also established for our approach. To enhance the finite sample performance, a data-driven thresholding and a two-stage iterative procedure are developed. To the best of our knowledge, screening for grouped variables rarely appeared in the literature, and this method can be regarded as an important and non-trivial extension of screening for individual variables. An extensive simulation study and a real data analysis demonstrate its finite sample performance.

THE COMPARISON OF ORAL HEALTH PROBLEMS WITH OTHER HEALTH PROBLEMS IN URBAN SCHOOL CHILDREN OF 10-14 YEARS.- A GROUP SCREENING

The overall health, well being, education, learning abilities, development of children, families and communities can be affected by oral health.However in the developing nations the importance of oral health in comparison to general health is minimal. Hence the present study was considered to evaluate and compare the oral health problems in relation to general health problems of the same children by a group screening method. A total of 592 children in the age group of 10–14 years were selected from class 6-class 10, the focus initially was on General Health screening, Vision Screening, oral health screening, tonsillar and auditory screening of students. Among these children 296 indiviulas were apparently healthy with no undiagnosed health aliments; the remaining children were having some health problems. In the present study 50% of children were apparently healthy, 19.9% had oral health problems, which was considerably higher than other health problems. In the present study the comparison of oral health problems occurence with general health problems shows statistically insignificance, In the present study the comparison of oral health problems occurrence with tonsillar, auditory and vision problems shows statistically significance. Based on these findings it can be suggested that in urban school children the undiagnosed oral health problems is higher than any other health aliments, which recommends promoting awareness on the oral health and importance of regular dental check up.

Group screening method for the statistical analysis of [formula omitted]-optimal mixed-level supersaturated designs

In this paper, we propose the application of group screening methods for analyzing data using E ( f N O D ) -optimal mixed-level supersaturated designs possessing the equal occurrence property. Supersaturated designs are a large class of factorial designs which can be used for screening out the important factors from a large set of potentially active variables. The huge advantage of these designs is that they reduce the experimental cost drastically, but their critical disadvantage is the high degree of confounding among factorial effects. Based on the idea of the group screening methods, the f factors are sub-divided into g “group-factors”. The “group-factors” are then studied using the penalized likelihood statistical analysis methods at a factorial design with orthogonal or near-orthogonal columns. All factors in groups found to have a large effect are then studied in a second stage of experiments. A comparison of the Type I and Type II error rates of various estimation methods via simulation experiments is performed. The results are presented in tables and discussion follows.

Hidden Aconite Poisoning: Identification of Yunaconitine and Related Aconitum Alkaloids in Urine by Liquid Chromatography-Tandem Mass Spectrometry

Poisoning from aconite occurs worldwide as a result of misuse of the potent plant. Laboratory investigation into suspected intoxication cases is challenging because the content of toxic aconitum alkaloids varies depending on the plant source, market processing, dosing protocol, hydrolytic degradation, and metabolic transformation. Using a triple-quadrupole tandem mass spectrometer, a group screening method was developed based on the mass-fragmentographic scheme of common aconitum alkaloids. The precursor-ion scans of m/z 105 and 135 permitted selective profiling of 14-O-benzoyl-norditerpenoids and the 14-O-anisoyl-norditerpenoids, respectively. Gradient reversed-phase liquid chromatography minimized coelution of isobaric compounds. The screening protocol was applied to a clinical investigation of suspected herbal poisoning. In total, 15 urine samples were thus screened positive for aconitum alkaloid over 5 years. The diagnoses of aconite poisoning in 11 patients were firmly established based on the known prescription history and the positive urine finding. In four patients, without aconitum herbs being listed in the herbal prescriptions, contamination of the herbal remedies by aconite was suspected to be the hidden cause of their acute poisoning. Yunaconitne, a highly toxic aconitum alkaloid, was thus identified in human urine for the first time. The group screening method of aconitum alkaloids in urine is an important diagnostic aid for acute poisoning by aconites of an unclear origin.

A Web-Based Knowledge Elicitation System (GISEL) for Planning and Assessing Group Screening Experiments for Product Development

When planning experiments to examine how product performance depends on the design, manufacture and environment of use, there are invariably too few resources to enable a complete investigation of all possible variables (factors). We have developed new algorithms for generating and assessing efficient two-stage group screening strategies which are implemented through a web-based system called GISEL. This system elicits company knowledge which is used to guide the formulation of competing two-stage strategies and, via the algorithms, to provide quantitative assessment of their efficiencies. The two-stage group screening method investigates the effect of a large number of factors by grouping them in a first stage experiment whose results identify factors to be further investigated in a second stage. Central to the success of the procedure is ensuring that the factors considered, and their grouping, are based on the best available knowledge of the product. The web-based software system allows information and ideas to be contributed by engineers at different sites and allows the experiment organizer to use these expert opinions to guide decisions on the planning of group screening experiments. The new group screening algorithms implemented within the software give probability distributions and indications of the total resource needed for the experiment. In addition, the algorithms simulate results from the experiment and estimate the percentage of important or active main effects and interactions that fail to be detected. The approach is illustrated through the planning of an experiment on engine cold start optimization at Jaguar Cars.

Simulation analysis with group screening

Simulation of systems involve a number of factors or variables which act and interact to produce an output. When these factors are small in number the experimenter can control them effectively to obtain an optimal output, but when the number of factors increases then efforts to control all the factors effectively also increases and leads to wastage of resources and away from optimality. Very often in a system where a number of factors are involved only a few of them actively involve in producing the output. Therefore it is the interest of the researcher to detect those active factors, and experimental design is frequently applied. The detecting process is called as screening. Screening can done by factorial designs such as 2 K (full factorial design) or by 2 k-p (fractional factorial design). Even by the fractional factorial method, one might still need to run too many experiments. It is desired to accomplish the screening process in as few number of runs as possible using random balance design, super saturated design or group-screening. Of these methods group-screening has been identified by researchers such as Mauro, Smith, etc. as the most efficient tool. In this research we study the usage of group-screening method as a simulation analysis tool while improving the method from earlier researches. Performance and results are studied and provided in form of response surface figures and tables.

Optimization of multivariate simulation output models using a group screening method

Computer simulation is often used to identify the set of model variable values which appear to lead to optimal performance of the modeled system. When all variables are quantitative, experimental designs and procedures known as response surface methodology (RSM) can be used in dealing with this optimization problem. Previous research has emphasized the situation in which there are only two independent variables. If there are more than two independent variables, it becomes difficult to find optimization and the simulation processes may be expensive. Nevertheless, these are common circumstances. This paper proposes a methodology using a two-stage group screening experimental design to investigate which subset of the variables is most important in explaining the optimum response variable. A simulation of a new generation of flight simulator is analyzed to determine the values of six variables that will optimize the values of a dependent variable simultaneously. The group screening technique is a powerful and robust tool to implement response surface methodology strategies.

Isomerization of n-butene to isobutene : I. Selection of catalyst by group screening

The group screening method has been applied to the selection of the best catalyst for the isomerization of n-butene to isobutene, as the first step in a detailed program of investigations on the mechanism and kinetics of this reaction. Forty-six acid-solid catalysts grouped according to their chemical similarities into twelve groups have been tested for their performance in the conversion of n-butene to isobutene at 400 °C, atmospheric pressure and W F = 40 [ratio of the catalyst weight (g) to the feed rate (gmole/hr)] in an integral-type reactor. Constituent catalysts of the group which appeared to contain the most promising catalysts were then tested independently under identical conditions in order to identify the best catalyst. Fluorinated η-alumina (1% F) was found to be the best catalyst, with the highest activity (conversion), good selectivity, appropriate life, and excellent regenerability. It was possible to identify the best catalyst in only 17 runs by the group screening method as against a minimum of 46 that would have been necessary if all the catalysts had been assessed independently (excluding life and regeneration tests in both cases).

A Note on G. S. Watson's Paper ‘A Study of the Group Screening Method”

In a paper in this Journal, Watson (1961) has discussed the use of group screening methods for detecting effective factors. The methods are analogous to those suggested by Dorfman (1943) for the medical problem of detecting a rare defect among members of a large population by using pooled blood samples. One step in Watson's derivation of the probability that an effective factor is declared significant is invalid. The purpose of this note is to derive the correct expression for this probability and show how the correction affects the subsequent theory. None of Watson's qualitative statements about the efficiency of group screening methods nor his numerical example are seriously affected.

A Note on G. S. Watson's Paper 'A Study of the Group Screening Method"

A Note on G. S. Watson's Paper 'A Study of the Group Screening Method"

Addendum: A Study of the Group Screening Method

Addendum: A Study of the Group Screening Method

A Study of the Group Screening Method

A Study of the Group Screening Method

A Study of the Group Screening Method

This paper discusses the problem of group screening methods wherein f factors are sub-divided into groups of k factors each, forming g “group-factors”. The group factors are then studied using a Plackett and Burman design in Q + 1 runs. The two versions of the group factors are formed by maintaining all component factors at their upper and lonrer levels respectively. All factors in groups found to have a large effects are then studied in a second stage of experiments. The author discusses the problems of detection and false detection of factors, optimum group size, size of program, and the role of costs in this sequential form of experimentation.