Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques.

BACKGROUND: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. METHODS: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. RESULTS: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. CONCLUSIONS: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Group analysis of distance matrices.

Distance-based regression model has become a powerful approach to identifying phenotypic associations in many fields. It is found to be particularly useful for high-dimensional biological and genetic data with proper distance or similarity measures being available. The pseudo F statistic used in this model accumulates information and is effective when the signals, that is the variations represented by the eigenvalues of the similarity matrix, scatter evenly along the eigenvectors of the similarity matrix. However, it might lose power for the uneven signals. To deal with this issue, we propose a group analysis on the variations of signals along the eigenvalues of the similarity matrix and take the maximum among them. The new procedure can automatically choose an optimal grouping point on some given thresholds and thus can improve the power evidence. Extensive computer simulations and applications to a prostate cancer data and an aging human brain data illustrate the effectiveness of the proposed method.

Detection of Potentially Compromised Computer Nodes and Clusters Connected on a Smart Grid, Using Power Consumption Data.

Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.

Demographic Seasonality.

The seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as 'non-flatness' throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.

Simulation-based evaluation of the linear-mixed model in the presence of an increasing proportion of singletons.

Data in medical sciences often have a hierarchical structure with lower level units (e.g. children) nested in higher level units (e.g. departments). Several specific but frequently studied settings, mainly in longitudinal and family research, involve a large number of units that tend to be quite small, with units containing only one element referred to as singletons. Regardless of sparseness, hierarchical data should be analyzed with appropriate methodology such as, for example linear-mixed models. Using a simulation study, based on the structure of a data example on Ceftriaxone consumption in hospitalized children, we assess the impact of an increasing proportion of singletons (0-95%), in data with a low, medium, or high intracluster correlation, on the stability of linear-mixed models parameter estimates, confidence interval coverage and F test performance. Some techniques that are frequently used in the presence of singletons include ignoring clustering, dropping the singletons from the analysis and grouping the singletons into an artificial unit. We show that both the fixed and random effects estimates and their standard errors are stable in the presence of an increasing proportion of singletons. We demonstrate that ignoring clustering and dropping singletons should be avoided as they come with biased standard error estimates. Grouping the singletons into an artificial unit might be considered, although the linear-mixed model performs better even when the proportion of singletons is high. We conclude that the linear-mixed model is stable in the presence of singletons when both lower- and higher level sample sizes are fixed. In this setting, the use of remedial measures, such as ignoring clustering and grouping or removing singletons, should be dissuaded.

Effect of variance ratio on ANOVA robustness: Might 1.5 be the limit?

Inconsistencies in the research findings on F-test robustness to variance heterogeneity could be related to the lack of a standard criterion to assess robustness or to the different measures used to quantify heterogeneity. In the present paper we use Monte Carlo simulation to systematically examine the Type I error rate of F-test under heterogeneity. One-way, balanced, and unbalanced designs with monotonic patterns of variance were considered. Variance ratio (VR) was used as a measure of heterogeneity (1.5, 1.6, 1.7, 1.8, 2, 3, 5, and 9), the coefficient of sample size variation as a measure of inequality between group sizes (0.16, 0.33, and 0.50), and the correlation between variance and group size as an indicator of the pairing between them (1, .50, 0, -.50, and -1). Overall, the results suggest that in terms of Type I error a VR above 1.5 may be established as a rule of thumb for considering a potential threat to F-test robustness under heterogeneity with unequal sample sizes.

Assessing the impact of selection bias on test decisions in trials with a time-to-event outcome.

If past treatment assignments are unmasked, selection bias may arise even in randomized controlled trials. The impact of such bias can be measured by considering the type I error probability. In case of a normally distributed outcome, there already exists a model accounting for selection bias that permits calculating the corresponding type I error probabilities. To model selection bias for trials with a time-to-event outcome, we introduce a new biasing policy for exponentially distributed data. Using this biasing policy, we derive an exact formula to compute type I error probabilities whenever an F-test is performed and no observations are censored. Two exemplary settings, with and without random censoring, are considered in order to illustrate how our results can be applied to compare distinct randomization procedures with respect to their performance in the presence of selection bias. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Nuts and Bolts of Oil and Temperature in Topical Preparations of Salicylic Acid.

BACKGROUND: Traditional formulations of salicylic acid in ointment bases have disadvantages of being greasy and irritant due to free crystals. AIM: To explore the method for selection of oils and their ratio for topical preparation on the basis of equilibrium solubility study of salicylic acid and to evaluate temperature effect. MATERIALS AND METHODS: Scanning and calibration curve of salicylic acid in methanol were developed. Among available oils, those that had no interference of absorbance of salicylic acid were short-listed for screening purpose. Selections of oils were carried out on the basis of equilibrium solubility study. Compatibility study was made by Fourier Transform Infra-Red spectroscopy analysis. Primitive study of oil mixtures was done. Selections of the ratio of oils were carried out on basis of constrained simplex-centroid design. RESULTS: Salicylic acid had shown linearity in the range of 15-65 µg/mL in methanol at wavelength maximum (300 nm). From the equilibrium solubility study, Parker Neem® Oil (11.81 ± 0.5 mg/g), Isopropyl Myristate (11.29 ± 0.04 mg/g), Mogra Oil (9.62 ± 0.94 mg/g) were selected. The study possessed the same main Fourier transform infra-red peaks of salicylic acid in the salicylic acid-oils physical mixture. 58.64% Parker Neem® oil and 41.36% isopropyl myristate mixture was selected as optimized batch with the desirability of 0.391. CONCLUSION: The oils mixture could be selected for topical preparation of salicylic acid like paste, cream, ointment etc.

Meta-analysis of ratios of sample variances.

When conducting a meta-analysis of standardized mean differences (SMDs), it is common to use Cohen's d, or its variants, that require equal variances in the two arms of each study. While interpretation of these SMDs is simple, this alone should not be used as a justification for assuming equal variances. Until now, researchers have either used an F-test for each individual study or perhaps even conveniently ignored such tools altogether. In this paper, we propose a meta-analysis of ratios of sample variances to assess whether the equality of variances assumptions is justified prior to a meta-analysis of SMDs. Quantile-quantile plots, an omnibus test for equal variances or an overall meta-estimate of the ratio of variances can all be used to formally justify the use of less common methods when evidence of unequal variances is found. The methods in this paper are simple to implement and the validity of the approaches are reinforced by simulation studies and an application to a real data set.

An evaluation of constrained randomization for the design and analysis of group-randomized trials.

In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore, simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including the following: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative, while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.

Objective detection of auditory steady-state evoked potentials based on mutual information.

OBJECTIVE: Recently, we developed a metric to objectively detect human auditory evoked potentials based on the mutual information (MI) between neural responses and stimulus spectrograms. Here, the MI algorithm is evaluated further for validity in testing the auditory steady-state response (ASSR), a sustained potential used in objective audiometry. DESIGN: MI was computed between spectrograms of ASSRs and their evoking stimuli to quantify the shared time-frequency information between neuroelectric activity and stimulus acoustics. MI was compared against two traditional ASSR detection metrics: F-test and magnitude-squared coherence (MSC). STUDY SAMPLE: Using an empirically derived threshold (⊖MI=1.45), MI was applied as a binary classifier to distinguish actual biological responses recorded in human participants (n=11) from sham recordings, containing only EEG noise (i.e., non-stimulus-control condition). RESULTS: MI achieved high overall accuracy (>90%) in identifying true ASSRs from sham recordings, with true positive/true negative rates of 82/100%. During online averaging, comparison with two other indices (F-test, MSC) indicated that MI could detect ASSRs in roughly half the number of trials (i.e., ∼400 sweeps) as the MSC and performed comparably to the F-test, but showed slightly better signal detection performance. CONCLUSIONS: MI provides an alternative, more flexible metric for efficient and automated ASSR detection.

Simulation-based evaluation of the performance of the F test in a linear multilevel model setting with sparseness at the level of the primary unit.

In a linear multilevel model, significance of all fixed effects can be determined using F tests under maximum likelihood (ML) or restricted maximum likelihood (REML). In this paper, we demonstrate that in the presence of primary unit sparseness, the performance of the F test under both REML and ML is rather poor. Using simulations based on the structure of a data example on ceftriaxone consumption in hospitalized children, we studied variability, type I error rate and power in scenarios with a varying number of secondary units within the primary units. In general, the variability in the estimates for the effect of the primary unit decreased as the number of secondary units increased. In the presence of singletons (i.e., only one secondary unit within a primary unit), REML consistently outperformed ML, although even under REML the performance of the F test was found inadequate. When modeling the primary unit as a random effect, the power was lower while the type I error rate was unstable. The options of dropping, regrouping, or splitting the singletons could solve either the problem of a high type I error rate or a low power, while worsening the other. The permutation test appeared to be a valid alternative as it outperformed the F test, especially under REML. We conclude that in the presence of singletons, one should be careful in using the F test to determine the significance of the fixed effects, and propose the permutation test (under REML) as an alternative.

Comparison of old and new wheat cultivars in Iran by measuring germination related traits, osmotic tolerance and ISSR diversity.

A primary concern of modern plant breeding is that genetic diversity has decreased during the past century. This study set out to explore changes in genetic variation during 84 years of breeding by investigating the germination-related traits, inter-simple sequence repeat (ISSR) fingerprinting and osmotic stress tolerance of 30 Iranian wheat (Triticum aestivum L.) cultivars. Seeds were planted under control and osmotic stress (-2, -4 and -6 bar) in three replications. The ISSR experiment was carried out using 32 different primers. Genotypes were divided into two groups (old and new) each containing 15 members. The results of ANOVA showed that highly significant differences existed among genotypes and among growth conditions. The results showed that during breeding in some traits such as coleoptile length and seedling vigor index, a significant decrease has been occurred. New cultivars had a mean coleoptile length of 33 mm, shorter than that of old cultivars (42 mm) under osmotic stress of -6 bar. Genetic variance of root length, shoot length and seedling vigor index for old cultivars were 1.59, 1.93 and 45,763, respectively, significantly higher than those for new cultivars (0.55, 1.08 and 27,996, respectively). This difference was also verified by ISSR results as the polymorphism information content was 0.28 in old cultivars, higher than that of new cultivars (0.26). These results prove this claim that during breeding, genetic diversity has decreased for many germination-related traits and breeders are better to pay more attention to genetic diversity.

[Near infrared analysis of blending homogeneity of Chinese medicine formula particles based on moving window F test method].

Blending uniformity is essential to ensure the homogeneity of Chinese medicine formula particles within each batch. This study was based on the blending process of ebony spray dried powder and dextrin(the proportion of dextrin was 10%),in which the analysis of near infrared (NIR) diffuse reflectance spectra was collected from six different sampling points in combination with moving window F test method in order to assess the blending uniformity of the blending process.The method was validated by the changes of citric acid content determined by the HPLC. The results of moving window F test method showed that the ebony spray dried powder and dextrin was homogeneous during 200-300 r and was segregated during 300-400 r. An advantage of this method is that the threshold value is defined statistically, not empirically and thus does not suffer from threshold ambiguities in common with the moving block standard deviatiun (MBSD). And this method could be employed to monitor other blending process of Chinese medicine powders on line.

Estimating the number of pure chemical components in a mixture by X-ray absorption spectroscopy.

Principal component analysis (PCA) is a multivariate data analysis approach commonly used in X-ray absorption spectroscopy to estimate the number of pure compounds in multicomponent mixtures. This approach seeks to describe a large number of multicomponent spectra as weighted sums of a smaller number of component spectra. These component spectra are in turn considered to be linear combinations of the spectra from the actual species present in the system from which the experimental spectra were taken. The dimension of the experimental dataset is given by the number of meaningful abstract components, as estimated by the cascade or variance of the eigenvalues (EVs), the factor indicator function (IND), or the F-test on reduced EVs. It is shown on synthetic and real spectral mixtures that the performance of the IND and F-test critically depends on the amount of noise in the data, and may result in considerable underestimation or overestimation of the number of components even for a signal-to-noise (s/n) ratio of the order of 80 (σ = 20) in a XANES dataset. For a given s/n ratio, the accuracy of the component recovery from a random mixture depends on the size of the dataset and number of components, which is not known in advance, and deteriorates for larger datasets because the analysis picks up more noise components. The scree plot of the EVs for the components yields one or two values close to the significant number of components, but the result can be ambiguous and its uncertainty is unknown. A new estimator, NSS-stat, which includes the experimental error to XANES data analysis, is introduced and tested. It is shown that NSS-stat produces superior results compared with the three traditional forms of PCA-based component-number estimation. A graphical user-friendly interface for the calculation of EVs, IND, F-test and NSS-stat from a XANES dataset has been developed under LabVIEW for Windows and is supplied in the supporting information. Its possible application to EXAFS data is discussed, and several XANES and EXAFS datasets are also included for download.

Nonparametric tests for evaluation of biosimilarity in variability of follow-on biologics.

As more biologic products are going off patent protection, the development of follow-on biologic products (also known as biosimilars) has gained much attention from both the biotechnology industry and regulatory agencies. Unlike small molecules, the development of biologic products is not only more complicated but also sensitive to a small change in procedure/environment during the manufacturing process. In practice, biologics are expected to have much larger variation, which will potentially impact the product quality and potency. Thus, it is suggested that the assessment of biosimilarity between biologic products should take variability into consideration, in addition to average biosimilarity of endpoints of interest. In this article, we propose the use of nonparametric tests for evaluation of biosimilarity in variability between the follow-on biologic product and the reference product. Extensive simulations are conducted to compare the relative performance of the proposed methods with the adapted parametric F-test in terms of correctly concluding biosimilarity in variability. Under normality assumption, the proposed nonparametric tests are found to be comparably well with the adapted F-test. However, the proposed methods are more robust when the assumption is violated.

Generalized F test and generalized deviance test in two-way ANOVA models for randomized trials.

We consider the problem of detecting treatment effects in a randomized trial in the presence of an additional covariate. By reexpressing a two-way analysis of variance (ANOVA) model in a logistic regression framework, we derive generalized F tests and generalized deviance tests, which provide better power in detecting common location-scale changes of treatment outcomes than the classical F test. The null distributions of the test statistics are independent of the nuisance parameters in the models, so the critical values can be easily determined by Monte Carlo methods. We use simulation studies to demonstrate how the proposed tests perform compared with the classical F test. We also use data from a clinical study to illustrate possible savings in sample sizes.

Fit of biokinetic data in molecular radiotherapy: a machine learning approach.

BACKGROUND: In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ). METHODS: Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [131I]I-NaI. Test performances, defined as classification accuracy (CA) and percentage difference between the actual and the estimated area under the curve (Δτ), were compared with those obtained using AM varying the number of points (N) of the TACs. A comparison between AM and ML were performed using data of 20 real patients. RESULTS: As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, [Formula: see text] can reach down to - 67%, while using ML [Formula: see text] ranges within ± 25%. Using real TACs, there is a good agreement between τ obtained with ML system and AM. CONCLUSIONS: The employing of ML systems may be feasible, having both a better classification and a better estimation of biokinetic parameters.

Analysis of gene expression dynamics and differential expression in viral infections using generalized linear models and quasi-likelihood methods.

Introduction: Our study undertakes a detailed exploration of gene expression dynamics within human lung organ tissue equivalents (OTEs) in response to Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3) infections. Through the analysis of RNA-Seq data from 19,671 genes, we aim to identify differentially expressed genes under various infection conditions, elucidating the complexities of virus-host interactions. Methods: We employ Generalized Linear Models (GLMs) with Quasi-Likelihood (QL) F-tests (GLMQL) and introduce the novel Magnitude-Altitude Score (MAS) and Relaxed Magnitude-Altitude Score (RMAS) algorithms to navigate the intricate landscape of RNA-Seq data. This approach facilitates the precise identification of potential biomarkers, highlighting the host's reliance on innate immune mechanisms. Our comprehensive methodological framework includes RNA extraction, library preparation, sequencing, and Gene Ontology (GO) enrichment analysis to interpret the biological significance of our findings. Results: The differential expression analysis unveils significant changes in gene expression triggered by IAV, MPV, and PIV3 infections. The MAS and RMAS algorithms enable focused identification of biomarkers, revealing a consistent activation of interferon-stimulated genes (e.g., IFIT1, IFIT2, IFIT3, OAS1) across all viruses. Our GO analysis provides deep insights into the host's defense mechanisms and viral strategies exploiting host cellular functions. Notably, changes in cellular structures, such as cilium assembly and mitochondrial ribosome assembly, indicate a strategic shift in cellular priorities. The precision of our methodology is validated by a 92% mean accuracy in classifying respiratory virus infections using multinomial logistic regression, demonstrating the superior efficacy of our approach over traditional methods. Discussion: This study highlights the intricate interplay between viral infections and host gene expression, underscoring the need for targeted therapeutic interventions. The stability and reliability of the MAS/RMAS ranking method, even under stringent statistical corrections, and the critical importance of adequate sample size for biomarker reliability are significant findings. Our comprehensive analysis not only advances our understanding of the host's response to viral infections but also sets a new benchmark for the identification of biomarkers, paving the way for the development of effective diagnostic and therapeutic strategies.