Regression Estimates Research Articles

Mortality (Dis)Similarities in the Context of Demographic Aging: The Countries of Ex-Yugoslavia

Generally, mortality declines at all ages, with varying intensities, as a result of heterogeneous factors affecting human lifespan. This paper considers switching regression estimation for six ex-Yugoslavian countries with the specification of a time-varying transition probability model of crude death rate. A two-state Markov switching means VAR estimates are used in which the mean growth rate of crude death rate is subject to regime switching, where the errors follow a constant transition probability. The UN data were employed consisting of the crude mortality rate series containing the log difference of yearly crude mortality rate in the six countries of ex-Yugoslavia for 1990–2021. The results show that the estimates of coefficients on the intercept in the mean equation both differ from zero in Bosnia & Herzegovina, Serbia, Montenegro, and Slovenia and are with opposite and statistically significant signs only for Montenegro and Slovenia. The transition probability summaries show a higher probability of remaining in the first high regime state for Macedonia, Serbia, and Bosnia & Herzegovina (0.96, 0.94, and 0.87, respectively). The higher probability of remaining in the second low-medium regime state was found in Slovenia, Croatia, and Montenegro (0.96, 0.87, and 0.56, respectively). The appropriate expected durations in the first regime are approximately 26.41, 16.19, and 7.78 years for Macedonia, Serbia, and Bosnia & Herzegovina, and 28.30, 7.80, and 2.28 years were the corresponding expected durations in the second regime for Slovenia, Croatia, and Montenegro.

The Role of Financial Constraints in Shaping Non-Financial Firm Performance in Nigeria: Evidence from the KZ Index

This study examines the impact of financial constraints on the performance of non-financial firms in Nigeria, utilizing the KZ index as a measure of financial constraint. Employing a static panel regression estimation on a sample of 87 non-financial companies listed on the Nigerian Stock Exchange from 2016 to 2021, the study investigates the relationship between financial constraints and firm performance. The findings reveal that financial constraints significantly hinder firm performance, highlighting the importance of effective governance and access to external financing. The study contributes to the literature by providing insights into the financial challenges faced by non-financial firms in Nigeria and offers recommendations for policymakers and practitioners to improve corporate performance.

Green investments and inclusive growth: The case of the BRICS economies

Investments in green energy projects are highly critical to promoting climate transition and sustainable development. This objective motivates the investigation of the effect of green investments on inclusive growth. We develop an endogenous inclusive growth model that illustrates the channel through which private and public investments in green investments contribute to inclusive growth. By employing a dataset of the BRICS economies from 1990 to 2019, we fit both linear fixed-effects and fixed-effects threshold regression, the latter of which exhibits better prediction of green investment effects. Accordingly, the threshold regression estimates reveal that higher thresholds of green investments substantially accelerate inclusive growth in the BRICS economies. Furthermore, by controlling for the threshold role of financial sector development, we document the heterogeneous effects of green investments on inclusive growth. Unlike financial institutions’ development thresholds, a higher level of overall financial development and financial market development strengthens the positive role of green investments. Our findings offer impeccable policy actions to the governments and policymakers of the BRICS economies on pathways to promote green energy investments for sustainable development.

Determinants of rural households’ food security status in South Africa: lessons from covid-19 social relief grant recipients in NIDS-CRAM wave 5

The world experienced an unprecedented health shock (COVID-19) which caught everyone by surprise, and destabilized many sectors, especially the agri-food sector. This unpleasant situation caused upset in the food production and distribution chain, and continues to threaten the attainment of Sustainable Development Goal 2 which underscores zero hunger in society. Many people in South Africa were disproportionately affected with severe consequences on their livelihood activities, food security status, health status, and general welfare conditions; and as a result, social relief intervention was put in place or expanded. This study examined the factors influencing rural households’ food security status among the recipients of social relief grants in rural areas of South Africa. The study used a secondary dataset from Wave 5 of National Income Dynamics Study - Coronavirus Rapid Mobile Survey (NIDS-CRAM). The research applied descriptive statistics to describe the respondents’ socio-economic characteristics, and pattern of income flow. Food Insecurity Access Scale was employed to investigate the respondents’ food security status, while multinomial logistic regression was used to explore the determinants of food security. The results showed that the majority of the respondents were black (99%), female-headed (54.15%), dwelling in flats (74.01%), with secondary education (88.6%), and heavily dependent on government grants (58.61%). Also, most households were food secure (71.69%) during the pandemic. Moreover, households headed by men were more food secure (62.84%) compared to the ones headed by the female counterparts (37.08). Multinomial regression estimates revealed that formal education (p<0.1), employment status (p<0.05), dwelling type (p<0.01), as well as household size (p<0.01) significantly determined rural households’ food security status. The study concluded that social relief grants significantly reduced the food insecurity situation among the rural households in South Africa, and recommended that the government should ensure the continuation of the social relief of distress grant to the vulnerable people who are mostly domiciled in the rural areas, especially women who are disproportionately affected by these health shocks. Key words: Covid-19, social grant, rural households, food security, South Africa

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower’s distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower’s distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings.

Unlocking the potential of CYGNSS for pan-tropical inland water mapping through multi-source data and transformer

Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

Parametric and semiparametric approaches for copula-based regression estimation

Based on the normality assumption on dependent variable, regression analysis is one of the most popular statistical techniques for studying the dependence between response and explanatory variables. However, violation of this assumption in the data makes regression analysis inappropriate in several real life situations. Copula is a powerful tool for modeling multivariate data and have recently been employed in regression analysis. The key concept behind copula-based regression approach is to formulate conditional expectation in terms of copula density and marginal distributions. In this paper, we explore parametric and semiparametric estimations of the copula-based regression function. The maximum likelihood (ML), inference functions for margins (IFM), and pseudo maximum likelihood (PML) techniques are adopted here for estimation purposes. Extensive numerical experiments are performed to illustrate the performance of the proposed copula-based regression estimators under specified and misspecified scenarios of copulas and marginals. Finally, two real data applications are also presented to demonstrate the performance of the considered estimators.

Multi-scale geographically weighted regression estimation of carbon storage on coniferous forests considering residual distribution using remote sensing data

The Above-Ground Carbon (AGC) is an important indicator reflecting the carbon sink function of forest ecosystems. The area of coniferous forests in China accounts for about 50 % of the national forest area. Accurately estimating the AGC of coniferous forests plays an important role in evaluating forest carbon sink function. This study takes coniferous forests in Chun’an, Zhejiang Province as an example, using Landsat 8 OLI as the remote sensing data source, and innovatively proposes a multi-scale Geographically Weighted Regression model (MGWR) combined with Co-Kriging (COK), namely MGWR-COK and Ordinary Kriging (OK), namely MGWR-OK, for AGC estimation. This method first processes Landsat8 OLI remote sensing data, extracts and screens variables, and then constructs an MGWR model with AGC survey samples to estimate AGC and calculate residuals; Afterwards, two models, COK and OK, were used to spatially estimate the residuals; Finally, the AGC estimation results are overlaid with the residual estimation results to obtain the spatial distribution of AGC in coniferous forests. The study indicates that: (1) The selected remote sensing feature variables such as Optimized Soil-Adjusted Vegetation Index (OSAVI), Normalized Difference Vegetation Index (NDVI), Entropy (B3_ent_1 and B2_ent_2), Correlation (B4_corr and B5_corr_2) of texture information, B754 with band combination and elevation have a significant impact on AGC estimation. (2) The AGC accuracy R2 estimated by MGWR-OK and MGWR-COK are 0.837 and 0.857, respectively, which are 6 % and 8 % higher than the MGWR model, and the root mean square error is also reduced by 10 % and 12 %, respectively. This indicates that the combination of MGWR and Kriging interpolation can effectively reduce its spatial estimation error. (3) The range of AGC values estimated by the MGWR-COK model is 0.189–62.591 Mg ∙ hm−2, with a mean of 28.795 Mg ∙ hm−2. The spatial distribution shows a characteristic of high in the southeast and low in the northwest, which is consistent with the actual situation in the study area.

Chief Sustainability Officer (CSO) power and sustainability-based compensation for climate change disclosure and carbon performance: is it different for developed and developing nations?

Purpose Companies are increasingly appointing a Chief Sustainability Officer (CSO) to anchor the need to highlight climate change at the senior management level. This study aims to examine how CSO power and sustainability-based compensation influence climate reporting and carbon performance. Design/methodology/approach Using one of the largest data sets to date, consisting of 18,834 company years through the author’s observations, spanning an 11-year period (2011–2021) in 33 countries. This paper used quantitative methods – specifically, ordinal logistic regression estimation. This paper measures the level of climate change disclosure based on the carbon disclosure leadership methodology. Carbon performance is based on the intensity of carbon emissions (Scope 1, Scope 2), which is a quantitative and relatively more objective measure. Findings The results suggest that climate change disclosure continued to increase and the carbon emissions intensity of the companies in this study gradually decreased over the sample period. This paper finds that the presence of the CSO within the top management team has a positive and significant influence on the level of information on climate change of the companies in the sample. This finding confirms the idea that the managerial capacity of CSOs motivates the disclosure of climate change. The empirical results confirm that there are differences in the role that the CSO and sustainability-based compensation play in influencing the quality of climate information disclosure in developed and developing countries. Originality/value The recourse on a mixed theoretical framework, which highlights upper echelons theory, argues the understanding of the role of CSOs in explaining the relationship between climate change disclosure–carbon performance relationship. The novelty of the study lies in the approaches adopted to describe the quality of climate change disclosure. To control for endogeneity, this paper uses a difference-in-difference analysis by adding a firm to the Morgan Stanley Capital International index as an exogenous shock.

Role of China’s Outward Direct Investment and Outward Lending on Economic Growth of South Asia Countries

This work analyses whether China’s lending and outward direct investment impact the economic growth of South Asian countries. This study used panel data for the years 2004 to 2019 of selected South Asian countries. For empirical estimation, it employed Pooled Ordinary Least Square for baseline regression estimates and to control for possible endogeneity it used fully FOLS, DOLS, 2SLS, and GMM for robustness checking. Furthermore, it also employed Quantile regression for the validity of the results. The empirical estimates report that Chinese lending and FDI have a positive and significant influence on the growth performance of South Asia countries. In addition, the marginal contribution of bilateral trade to South Asian country’s economic growth is nonlinear. Finally, there exists a bidirectional causality between lending and economic growth and a unidirectional causality between GDP and ODI.

Financial inclusion and financial crisis: Arguments, stylized facts and evidence

AbstractThe literature has examined the relationship between financial inclusion and financial stability, but no studies have examined the relationship between financial inclusion and financial crisis. This study examines the effect of financial inclusion on financial crisis using data from 28 countries from 2006 to 2017. Three stylised facts were established based on real‐world observation. One, the level of financial inclusion, in terms of number of bank depositors, decreases during domestic financial crisis. Two, the level of financial inclusion, in terms of ATM penetration, does not decrease during global and domestic financial crises. Three, the level of financial inclusion, in terms of number of bank branch, decreases during global and domestic financial crises and the contraction is stronger during a domestic financial crisis. Using the panel regression, logit and probit regression estimation methods, the empirical results show that low levels of financial inclusion, measured by fewer bank depositors and fewer bank branches, increase the likelihood that a financial crisis will occur. Low levels of financial inclusion, measured by fewer bank depositors, increase the likelihood that a financial crisis will occur in low financial‐inclusion countries. In contrast, greater ATM penetration increases the likelihood that a financial crisis will occur in low financial‐inclusion countries. The interaction analyses show that all indices of financial inclusion have a joint positive impact on financial crisis, implying that high levels of financial inclusion increases the likelihood that a financial crisis will occur.

Innovation and economic growth: An empirical analysis for African countries

In recent decades, innovation has become recognized as a key driver of economic growth. This study investigated the relationship between innovation and economic growth in 32 African countries from 2006 to 2017. The linear regression panel corrected standard errors (PCSEs) regression estimation was used to analyze the data. PCSE estimation accounted for heteroskedasticity and possible contemporaneous correlation across panels. The findings showed a positive association between the innovation index and economic growth. This result remained true even when research and development (R&D) expenditure (the conventional measure of innovation) was considered for a handful of countries. This finding highlights the significance of innovation in fostering economic growth. The study also found that domestic investment and human capital encouraged economic growth. The study estimated an endogenous growth model with an alternative measure of innovation. Based on the findings, the study recommends that African countries provide financial and material support for R&D in public and private institutions through funding, imparting entrepreneurial research attitudes in academics, and providing an enabling environment for business enterprises.

A kernel-type regression estimator for NMAR response variables with applications to classification

This work deals with the problem of nonparametric estimation of a regression function when the response variable may be missing according to a not-missing-at-random (NMAR) setup. To assess the theoretical performance of our estimators, we study their strong convergence properties in Lp norms where we also look into their rates of convergence. We also study applications of our results to the problem of statistical classification in semi-supervised learning.

Digital finance and MSME performance in India: evidence from World Bank Enterprise Survey data

PurposeThe impact of digitalisation on smaller firms remains sparsely studied across emerging economies. The paper aims to examine the relationship between digital adoption and multiple performance parameters of micro, small and medium enterprises (MSME) in a prominent emerging economy.Design/methodology/approachThe study employs data from the World Bank Enterprise Survey (WBES) 2022, capturing 9,024 Indian MSME firms spread across the country. Performance indicators are derived from growth in sales, employment and labour productivity (LPROD). Multiple regression estimates are derived that also correct for sample selection bias using Heckman’s two-step process.FindingsDigital proliferation is found to increase as firms mature up in terms of age, size and constitution. A significant difference could also be observed in business performance across digital and non-digital businesses, with sales growth (SG) and productivity higher for digital firms. Digital financial variables are found to have a significant impact on SG but not as much in the case of employment growth and LPROD. The results are robust to correction for sample selection bias in digital adoption using inverse mills ratio (IMR).Practical implicationsThe study highlights digital adoption gaps across various strata of MSMEs, highlighting lower adoption when firms are younger, smaller and lacking formal constitutional setup. Digital variables indicating positive association with SG highlight the need for concerted efforts at the public policy level for building appropriate skills and infrastructure for micro and small enterprises to boost their digital adoption to promote growth.Originality/valueThere is a lack of micro-level empirical evidence measuring the impact of advanced digital technology usage on multiple aspects of enterprise performance amongst micro and small firms. The study deploys unique digital variables including TReDS and use of online credit applications to assess the impact on business performance. The findings provide insights for practice and public policy, besides making the case for a higher focus on launching digital initiatives for smaller enterprises.

Weighted penalized m-estimators in robust ridge regression: an application to gasoline consumption data

ABSTRACT The OLS and ridge regression (RR) estimators are adversely affected, when the problem of multicollinearity and y-direction outliers occur together. The robust ridge regression with penalized parameters offers biased estimators with lower variance than OLS and RR. The optimal penalized parameter value is crucial for balancing variance and bias. This study proposes three new weighted robust penalized M-estimators to address both multicollinearity and y-direction outliers. Their performance is evaluated against OLS, RR and existing penalized M-estimators using Monte Carlo simulations based on mean squared error (MSE). The new estimators exhibit lower MSE in the presence of multicollinearity and y-direction outliers. A real data application on gasoline consumption demonstrates the superior performance of the new estimators.

Effectiveness of Iterative Learning Control for Vision-Based Tracking in Repeated Tasks Under Varying Lighting Conditions

Among available techniques for lateral tracking in autonomous driving, none possesses the capability to learn from past behaviors and progressively reduce lateral errors. In contrast, our proposed iterative learning control (ILC) scheme significantly enhances tracking performance in vision-based classical control, particularly in challenging environmental conditions. Our vision-based control system incorporates real-time semantic segmentation using the ENet model for unconstructed road area extraction. Linear regression estimates steering adjustments, while a visual PID controller maintains the vehicle’s position at the road’s centerline. This control system’s performance varies with lighting conditions, notably in dense shade, where the vehicle tends to deviate from the desired path. To assess ILC’s potential in error reduction over successive trials, we examined various ILC structures and compared them with a pure PID controller. Despite challenges in directly comparing PID and ILC designs due to changing lighting conditions during experiments, ILC consistently reduced tracking errors and improved path alignment with each iteration. Notably, the degree of error reduction became more pronounced with a greater number of learning gains in the ILC design. Our experimental results underscore the overall effectiveness of ILC in tracking the desired path in autonomous driving scenarios, particularly in varying environmental conditions.

An empirical investigation of capital flight and economic growth nexus in Africa

ABSTRACT This paper investigates the impact of capital flight on economic growth in Africa using 54 African countries from 2000 to 2021. The study employed the GMM and the dynamic panel threshold regression estimation techniques. GMM results revealed that capital flight negatively and significantly affects economic growth in Africa. The study also revealed that external debt repayment and outward foreign direct investment, proxies of capital flight, have a negative and significant effect on economic growth in Africa, except foreign portfolio investment outflow, which positively and insignificantly influences economic growth. Again, the dynamic panel threshold estimation results also revealed that capital flight negatively affects economic growth at all levels of capital flight in Africa. The study also revealed that all the proxies of capital flight, external debt repayments, foreign portfolio investment outflows, and outward foreign direct investment negatively and significantly affect economic growth above a certain threshold. However, external debt repayment has an insignificant negative effect on economic growth below the threshold in Africa. Based on the findings, African countries should implement stringent policies to curb capital flight and regulate external debt repayments and outward foreign direct investment to enhance economic growth.

Evaluating food security and nutritional pathways of rural farm families: Empirical evidence from northeast India

The study aimed to ascertain a relationship between agricultural status, socioeconomic factors, and nutrition of farm families. The study was conducted in selected villages in the West Garo Hills district of Meghalaya, using Stratified Random Sampling (St. RS). Using pretested interview schedules, we collected primary data from respondents in 2020 and 2021, focusing on socioeconomic variables, body mass index, and income from agriculture and related sectors. The data was analysed using correlation analyses and separate combined regression estimates for each year and month were obtained. Results from the study indicate that agricultural income significantly influenced nutritional status (p < 0.05) and household income growth was also found significant. The region's agricultural production of cereals, pulses, and vegetables was insufficient, as was the production of meat and meat products, milk, and milk products. Hence, expenditure towards purchasing the above food groups from the market was found to be significant (p < 0.05). Therefore, the markets near the mainland especially in the hilly region play a crucial role in the nutritional pathway of rural farm families.

Does No Mean No? Situational and Dispositional Factors Influence Emerging Adult Men's Intentions to Use Assault Tactics in Response to Women's Sexual Refusal During Hookups.

Hookups can result in sexual assault when men do not listen to requests from women to stop. It is thus important to identify factors that influence men's decisions to override direct refusals in these situations. Presently, we administered first-person vignettes depicting a prototypical hookup wherein the woman refuses the man's attempt to escalate intimacy. Using a national sample of emerging adult men (N = 420), we found that they on average did not completely rule out coercive or forcible tactics, but those elevated on rape myth acceptance, hypermasculinity, and psychopathy were uniquely at risk of assault when controlling for several other traits known to correlate with rape. Participants also reported being likelier to use coercive sexual practices when refusals occurred at higher levels of sexual intimacy already attained. Notably, diagnostic analyses revealed that a subset of men had a disproportionate influence on the regression estimates, and that these men were not only elevated across a range of assault-relevant traits, but also endorsed higher likelihoods of using coercion and force in the face of female sexual refusal. Although removal of these cases did not substantively alter the results, exploratory analyses revealed that these individuals responded differently to situational factors in ways that suggested sexual opportunism. Theoretical and practical implications of these findings are discussed.

Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts.

Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts. Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD. There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable. This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.