Integrated Analysis Approach Research Articles

MIMR: Development of a Web-Based System for miRNA and mRNA Integrated Analysis.

The human body is a complex network of systems that is harmonized with multiple biological components. To understand these interactions is very challenging. With rapid development of advanced sequencing technologies, massive amounts of data such as mRNA, miRNA are rapidly accumulated. The integrated analysis of mRNA-miRNA has brought an extensive understanding of complex biological systems and pathological mechanisms. MicroRNAs (miRNAs) are small non-coding RNAs that intricately regulate target gene products, resulting in the inhibition of gene expression. While these miRNAs play crucial roles in essential biological processes-ranging from immunity and metabolism to cell death-their specific impacts on diseases remain unknown. Recent studies have been focused on the integration of miRNA and mRNA expression to reveal the underlying biological pathways and mechanisms responsible for disease manifestation. We proposed a novel approach for integrative analysis of miRNA and mRNA expression data and developed MIMR (Integrative Analysis of miRNA and mRNA), a web-based application that leverages the Random Walk with Restart (RWR) algorithm. MIMR incorporates both direct and indirect interactions by utilizing protein-protein interaction (PPI) networks and experimentally validated mRNA-miRNA target interactions. MIMR provides comprehensive results, including novel pathological pathways associated with a specific disease and interactive network diagrams representing the mRNAs and miRNAs. We applied it to Alzheimer and breast cancer data and successfully identified the novel biological pathways related to these diseases. In summary, MIMR will offer a deeper insight into the hidden mechanisms of diseases and identify potential therapeutic strategies through integrated analysis of miRNAs and mRNAs.

An Efficient Integral Approach for Kinematic Reliability Sensitivity Analysis of Industrial Robots

Abstract Assessment of the reliability and reliability sensitivity of positioning accuracy for industrial robots subject to aleatoric and epistemic uncertainties registers a challenging task. This study proposes a new optimized moment-based method for kinematic reliability analysis and its sensitivity analysis, which incorporates the sparse grid (SPGR) technique and the saddlepoint approximation (SPA) method. To start with, the positioning accuracy reliability and its sensitivity models of industrial robots are established via computational optimization techniques and kinematic criteria. The kinematic accuracy reliability and its sensitivity are then calculated. Specifically, the sparse grid technique is adopted to approach the positioning error statistical moments and moment sensitivities. On this basis, positioning accuracy reliability bounds and reliability sensitivity bounds are obtained by the saddlepoint approximation method and optimization techniques. Finally, two practical examples are implemented to demonstrate the proficiency of the currently proposed method against Monte Carlo simulation (MCS) results. The results show that the currently proposed method exhibits superior computational accuracy and efficiency in kinematic reliability and its sensitivity analyses for industrial robots.

Neil Armstrong’s digital twin: An integrative approach for movement analysis in simulated space missions

Neil Armstrong’s digital twin: An integrative approach for movement analysis in simulated space missions

Unveiling Commonalities and Differences in Genetic Regulations via Two-Way Fusion.

Understanding the genetic regulation, for example, gene expressions (GEs) by copy number variations and methylations, is crucial to uncover the development and progression of complex diseases. Advancing from early studies that are mostly focused on homogeneous groups of patients, some recent studies have shifted their focus toward different patient groups, explored their commonalities and differences, and led to insightful findings. However, the analysis can be very challenging with one GE possibly regulated by multiple regulators and one regulator potentially regulating the expressions of multiple genes, leading to two distinct types of commonalities/differences in the patterns of genetic regulation. In addition, the high dimensionality of both sides of regulation poses challenges to computation. In this study, we develop a two-way fusion integrative analysis approach, which innovatively applies two fusion penalties to simultaneously identify commonalities/differences in the regulated pattern of GEs and regulating pattern of regulators, and adopt a Huber loss function to accommodate the possible data contamination. Moreover, a simple yet efficient iterative optimization algorithm is developed, which does not need to introduce any auxiliary variables and extra tuning parameters and is guaranteed to converge to a globally optimal solution. The advantages of the proposed approach are demonstrated in extensive simulations. The analysis of The Cancer Genome Atlas data on melanoma and lung cancer leads to interesting findings and satisfactory prediction performance.

Acceptability, facilitators, and barriers to a hypothetical HIV vaccine in the pre-exposure prophylaxis era

ABSTRACT Little is known about the pre-implementation context for a preventive HIV vaccine. We conducted interviews of individuals in Philadelphia recruited at Penn clinics and community-based organizations serving LGBTQ-identifying persons of color who 1) were cisgender men who had sex with men, or were transgender-identified, 2) had a sexually transmitted infection in the last 12 months, or sex with multiple partners within the last two weeks. We assessed acceptability, facilitators, and barriers to a hypothetical HIV vaccine using an integrated analysis approach. We interviewed 30 individuals between 2/2023-9/2023. Participants were supportive of an HIV vaccine and reported that they would strongly consider receiving one if one became available. Participants contextualized a hypothetical vaccine with the current HIV prevention context, primarily pre-exposure prophylaxis (PrEP), indicating that they would evaluate any future vaccine in comparison to their experience within the PrEP landscape. Reported facilitators for a hypothetical HIV vaccine included vaccine access, knowledge, and understanding; their risk for HIV exposure; and perceived benefits of the vaccine. Barriers included lack of understanding of the purpose of a vaccine, stigma surrounding HIV and sexual practices that may surface towards people who seek vaccination, and potential issues with effectiveness, side effects, or lack of availability.

Small Municipalities in the Amazon under the Risk of Future Climate Change

The focus of this work is on small municipalities (population below 50 thousand inhabitants) that cover around 87% of the territory of the Brazilian Legal Amazon (BLA). Based on a comprehensive integrated analysis approach using the three components hazard (climate extremes from CMIP6 future scenarios), exposure (directly affected population), and vulnerability (subdimensions of susceptibility and coping/adaptive capacity by using multidimensional indicators), the latter two using current datasets provided by the official Census IBGE 2022, we document a quantitative assessment of the risk R of natural disasters in the BLA region. We evidenced a worrying and imminent intensification of the curve of R in most Amazonian municipalities over the next two 25-year periods. The overall results of the highest proportions of R (total municipalities affected) pointed out the Amazonas, Roraima, Pará, and Maranhão as the main states, presenting projected categories of R high in the near future (2015 to 2039) and very high in the far future (2040 to 2064). The detailed assessment of the susceptibility and coping/adaptive capacity allowed us to elucidate the principal indicators that aggravate the degree of vulnerability: economy, the precariousness of urban infrastructure, medical services, communication, and urban mobility, whose combined factors, unfortunately, reveal a widespread poverty profile along the small Amazonian municipalities. Our scientific findings can assist decision makers in targeted strategies planning and public policies to minimize and mitigate ongoing and future climate change.

Process optimization and mechanism for the selective extraction of copper(ii) from polymetallic acidic solutions using a polymer inclusion membrane (PIM) with Mextral®5640H as the carrier.

This study introduces a novel approach by integrating solvent extraction and polymer inclusion membrane (PIM) separation technologies to engineer an innovative PIM membrane material for the selective separation and enrichment of strategic metals, particularly copper, from polymetallic acidic solutions. The primary objectives were to streamline the technological process, reduce production costs, and enhance separation coefficients between copper and other metals. The optimal extraction conditions were determined as a mass fraction of Mextral®5640H : PVC : NPOE = 3 : 3 : 4, extraction temperature of 35 °C, and 0.9 mol L-1 H2SO4 in the stripping solution. Under these conditions, we achieved remarkable extraction efficiencies, with copper reaching 100%, and the separation coefficients between Cu2+ and Ni2+, Co2+, and Zn2+ exceeding 106. To elucidate the substantial differences in extraction performance between Cu2+ and the other metals (Ni2+, Co2+, and Zn2+), we employed an integrative analytical approach that combines FT-IR spectroscopy, BET analysis, and theoretical calculations. All extracted complexes demonstrated molecular sizes compatible with PIMs, underscoring the critical role of stability and back-extraction performance in the selective extraction of these metal ions.

Metabolomic and transcriptomic correlative analyses in germ free mice link probiotic-associated metabolites to host intestinal fatty acid metabolism and b-oxidation

While a transcriptomics-led examination of the effects of probiotics offers valuable insights into their influence on host gene regulation, the dynamic interactions between host metabolism and probiotics require an integrated analysis and multiomics approach to provide deeper insight into the underlying molecular mechanisms governing these associations. Here, we developed a high throughput bioinformatic tool ‘metabolome-transcriptome correlation analysis’ (METRCA, in press) that can determine whether a certain gene-metabolite association is up or downregulated and predict whether a metabolite’s or gene’s tissue location impacts its interaction with one another. To test this, we analyzed by LCMS and bulk-RNA sequencing then assessed linkages between serum as well as fecal metabolites and disease- or metabolism-associated genes in the ileum of gnotobiotic mice fed either a tryptophan (trp)-deficient or -suffcient diet then mono-associated with Lactobacillus rhamnosus GG (LGG) or administered PBS (control). We discovered significant associations, mainly in LGG mice fed trp, between metabolites and ileal genes involved in fatty acid metabolism (FAM) and oxidation (FAO). This association was specific and not observed in GF mice gavaged with PBS, monocolonized with R. gnavus, or fed trp- diets, and in SPF mice. Many fecal trp metabolites like indole-3-acetamide and serum trp metabolites like indole acetonitrile were significantly, positively correlated with important intestinal FAM genes Dgat1 and Mgat2. Likewise, numerous fecal metabolites like 5-hydroytrp and several serum trp metabolites like serotonin emerged as positively correlated metabolites with the rate-limiting FAO genes Acaa2 and Cpt2. Serum indoles were more strongly associated with both FAM and FAO genes than fecal indoles. Few fecal and serum metabolites were found negatively correlated with FAM and FAO genes. Since most are positive correlators, these findings suggest a testable hypothesis that LGG- and trp-dependent metabolites increase the intestinal metabolism of dietary lipids, providing a mechanism to previous findings that LGG supplementation in mice protected against diet-induced obesity and fatty liver disease. Analytical approaches like METRCA can thus be used to extract valuable information from metabolite and RNA-Seq datasets, generating novel hypotheses that may discover new interactions between microbe- or nutrient-associated metabolites and host metabolic or disease-related pathways. NIHR01-AT010243 (NG,RF), R01DK119198 (NG), NSF 1754783 (RF)). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Status and perspective of soil fauna eco-geography in China

As regulators of the surface land processes, soil fauna communities are the vital foundations for healthy terrestrial ecosystems. Soil fauna have been studied in China for more than 70 years. Great progresses have been achieved in exploring soil fauna species composition and geographical distribution patterns. Soil fauna eco-geography, as a bridge between soil fauna geographic patterns and ecosystem services, has a new development opportunity with the deep recognition of soil fauna ecological functions. Soil fauna eco-geography research could be partitioned into four dimensions including the spatio-temporal patterns of: 1) the apparent characteristics of soil fauna community, such as species composition, richness and abundance; 2) the intrinsic characteristics of soil fauna community, such as dietary and habits; 3) soil fauna-related biotic and abiotic interactions especially those indicating drivers of soil fauna community structure or shaping the roles of soil fauna in ecosystems; and 4) soil fauna-related or -regulated key ecological processes. Current studies focus solely on soil fauna themselves and their geographical distributions. To link soil fauna geography more closely with ecosystem services, we suggested that: 1) converting the pure biogeography studies to those of revealing the spatio-temporal patterns of the soil fauna-related or regulated key relationships and ecological processes;2) expanding the temporal and spatial scales in soil fauna geographical research;3) exploring the integrated analysis approach for soil fauna-related data with multi-scales, multi-factors, and multi-processes;and 4) establishing standard reference systems for soil fauna eco-geographical researches. Hence, the change patterns of ecological niche of soil fauna communities could be illustrated, and precision mani-pulations of soil fauna communities and their ecological functions would become implementable, which finally contributes to ecosystem health and human well-being.

Integrated Proteomics and Protein Co-expression Network Analysis Identifies Novel Epileptogenic Mechanism in Mesial Temporal Lobe Epilepsy.

Over 50 million people worldwide are affected by epilepsy, a common neurological disorder that has a high rate of drug resistance and diverse comorbidities such as progressive cognitive and behavioural disorders, and increased mortality from direct or indirect effects of seizures and therapies. Despite extensive research with animal models and human studies, limited insights have been gained into the mechanisms underlying seizures and epileptogenesis, which has not translated into significant reductions in drug resistance, morbidities, or mortality. To better understand the molecular signaling networks associated with seizures in MTLE patients, we analyzed the proteome of brain samples from MTLE and control cases using an integrated approach that combines mass spectrometry-based quantitative proteomics, differential expression analysis, and co-expression network analysis. Our analyses of 20 human brain tissues from MTLE patients and 20 controls showed the organization of the brain proteome into a network of 9 biologically meaningful modules of co-expressed proteins. Of these, 6 modules are positively or negatively correlated to MTLE phenotypes with hub proteins that are altered in MTLE patients. Our study is the first to employ an integrated approach of proteomics and protein co-expression network analysis to study patients with MTLE. Our findings reveal a molecular blueprint of altered protein networks in MTLE brain and highlight dysregulated pathways and processes including altered cargo transport, neurotransmitter release from synaptic vesicles, synaptic plasticity, proteostasis, RNA homeostasis, ion transport and transmembrane transport, cytoskeleton disorganization, metabolic and mitochondrial dysfunction, blood micro-particle function, extracellular matrix organization, immune response, neuroinflammation, and cell signaling. These insights into MTLE pathogenesis suggest potential new candidates for future diagnostic and therapeutic development.

Economic Fraud and Associated Risks: An Integrated Bibliometric Analysis Approach

This study offers a comprehensive insight into the realms of economic fraud and risk management, underscoring the necessity of adaptability to evolving technologies and shifts in financial market dynamics. Through the application of bibliometric methodologies, this study meticulously maps the relevant literature, delineating influential works, notable authors, collaborative networks, and emerging trends. It reviews key research contributions within the field, alongside reputable journals and institutions engaged in academic research. The examination highlights the logical, conceptual, and social interconnections that define the landscape of economic fraud and associated risks, elucidating how these findings inform the understanding, mitigating, and combating of the risk of fraud. Our bibliometric analysis methodology is grounded in the utilization of the Scopus database, employing rigorous filtering and extraction processes to obtain a substantial corpus of pertinent articles. Through a fusion of performance analysis and science mapping, our investigation elucidates central themes and visually represents the interrelationships between studies. Our research outcomes underscore the frequency of paper publications across diverse regions, with particular emphasis on the predominant scientific output from the US and China. Additionally, trends in academic citations are identified, indicative of the significant impact of papers on academic research and the formulation of public policies. By means of bibliometric analysis, this study not only consolidates existing knowledge but also catalyzes the exploration of future research trajectories, emphasizing the imperative of addressing these issues with heightened scientific rigor.

ISLAMIC LAW AND LOCAL WISDOM: EXPLORING LEGAL SCIENTIFIC POTENTIAL IN INTEGRATING LOCAL CULTURAL VALUES

This study provides a richer and more holistic view of how Islamic law and local cultural values can be integrated effectively and harmoniously in regulating the lives of heterogeneous Muslim communities. The research method adopted in this study is library research with an integrative and comprehensive literature analysis approach to identify concepts, theories, techniques, and findings that have been put forward by previous researchers regarding the relationship between Islamic law and local wisdom by looking for patterns, similarities, differences, and trends that emerge from the analyzed literature. The results show that Islamic law, through its flexible conceptual framework, facilitates the integration of local cultural values into legal practice, maintaining its relevance in diverse social and cultural contexts. This integration process not only strengthens local cultural identity in the face of globalization but also supports social harmony by defusing cultural conflicts and strengthening community coherence. Furthermore, it creates an inclusive and fair legal system, recognizing and respecting individual diversity. This integration contributes to knowledge development by demonstrating how laws adaptive to cultural contexts can support sustainability and social justice in heterogeneous societies.

Revealing the oncogenic role of elevated GNL3L expression in esophageal squamous cell carcinoma: insights into the STAT3 pathway.

Esophageal squamous cell carcinoma (ESCC) patients carries a poor prognosis, with limited effective therapeutic targets. This study aimed to clarify the clinical significance of guanine nucleotide-binding protein like 3-like (GNL3L) protein expression in ESCC and its role in malignant progression. GNL3L expression and associated cancer-promoting pathways in ESCC were interrogated via bioinformatics analysis through use of The Cancer Genome Atlas (TCGA) database. Subsequent verification of GNL3L protein expression in ESCC, coupled with clinical data, was conducted through immunohistochemistry and followed by a comprehensive prognostic analysis. We further investigated potential signaling pathways facilitating ESCC progression, employing a combination of bioinformatics analysis and immunohistochemical (IHC) experiments. Bioinformatics analysis unveiled a significant elevation in GNL3L expression, particularly in gastrointestinal tumors and ESCC. Immunohistochemistry confirmed elevated GNL3L expression in ESCC tissues. Regression analysis established a correlation between elevated GNL3L expression and advanced tumor node metastasis (TNM) stage, with high expression associated with poor prognosis in patients with ESCC. Our integrated approach of bioinformatics and IHC analysis indicated a potential role of the signal transducers and activators of transcription 3 (STAT3) signaling pathway in ESCC progression. High GNL3L expression significantly contributes to the malignant progression of ESCC. This study further elucidates the mechanisms driving ESCC progression and offers possible insights for more effective diagnosis and treatment strategies.

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.

The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.

Exploring the Efficacy of LightGBM, AdaBoost, and CatBoost in Alzheimer’s Disease Classification

Alzheimer’s disease (AD) is a major public health concern that requires prompt and accurate diagnosis to intervene effectively. We present a comprehensive machine learning system in our work that is specifically designed to classify AD. It incorporates various neuroimaging and clinical features, including air time1, disp index1, gmrt in air1, max x extension1, and max y extension1. We can decipher complex data correlations suggesting AD pathology using rigorous preprocessing and visualization methods, such as correlation heatmaps and 3D scatter plots. We can distinguish minute changes between AD, moderate cognitive impairment, and healthy controls using CatBoost, LightGBM, and AdaBoost classifiers. A thorough assessment of the model’s performance is provided by the rigorous evaluation metrics of accuracy, precision, recall, and F1-score, which are supplemented by detailed classification reports and confusion matrices. Learning curves also provide information about the generalization and flexibility of models. Our findings support integrated analysis approaches across many data modalities and highlight the revolutionary potential of machine learning in AD diagnosis. Developing customized treatment plans and cutting-edge clinical decision support systems is expected to improve patient outcomes and the standard of care for neurodegenerative diseases.

Abstract P25: Identification of biological components and novel clinical subsets of NPM1-mutated AML using an integrated, multi-omics approach

Abstract P25: Identification of biological components and novel clinical subsets of <i>NPM1</i>-mutated AML using an integrated, multi-omics approach

N6-methyladenosine-associated genetic variants in NECTIN2 and HPCAL1 are risk factors for abdominal aortic aneurysm

Although N6-methyladenosine (m6A) modification has been implicated in the pathogenesis of abdominal aortic aneurysm (AAA), the relationship between m6A-associated single nucleotide polymorphisms (m6A-SNPs) and AAA remains unknown. This study used integrative multi-omics analysis and clinical validation approaches to systematically identify potential m6A-SNPs connected with AAA risk. We found that rs6859 and rs10198139 could modulate the expression of local genes, NECTIN2 and HPCAL1, respectively, which exhibited upregulation in AAA tissues, and their risk variants were significantly correlated with an increased susceptibility to AAA. Incorporating rs6859 and rs10198139 improved the efficiency of AAA risk prediction compared to the model considering only conventional risk factors. Additionally, these two SNPs were predicted to be located within the regulatory sequences, and rs6859 showed a substantial impact on m6A modification levels. Our findings suggest that m6A-SNPs rs6859 and rs10198139 confer an elevated risk of AAA, possibly by promoting local gene expression through an m6A-mediated manner.

Integrative approach for landscape demography analysis of Plakad-Pa Pak-Tawan-Ok (Betta siamorientalis): Deciphering genetic and environmental factors in Eastern Thailand's conservation efforts

In recent decades, a global acceleration of urbanization has been observed, leading to significant habitat loss and fragmentation. Economic development in eastern Thailand has been promoted, but this has often coincided with unsustainable exploitation of natural resources. The expansion of urban and industrial areas in the region puts many organisms at risk of extinction owing to habitat loss. Plakad-Pa Pak-Tawan-Ok (Betta siamorientalis) is an endemic species found only in the eastern Thailand. In this study, the genetic diversity and population structure of 17 B. siamorientalis populations were analyzed using microsatellite genotyping. High genetic variability was observed. The STRUCTURE plot reveals large, distinct gene pools in the lower Prachin River and Phra Prong River Part 2. Migration analyses showed that the estimated gene flow between populations aligned with the dominant water flow in the flooding direction toward the mouth of the Bang Pakong River on the western side of eastern Thailand. Thus, this area must be considered a vital genetic resource. The pairwise fixation index values were statistically significant for all populations, including those within the same sub-watershed, indicating genetic differentiation among populations. This suggests relatively low gene flow between ecologically divergent populations. Mean annual total precipitation was also significantly correlated with genetic diversity. We propose the need for careful consideration and awareness of the planning and implementation of industrial development within eastern Thailand. Resource allocation in management efforts may be most effective when directed toward the preservation of habitat suitability, specifically in relation to precipitation and water resources, rather than prioritizing enhancements in landscape connectivity for B. siamorientalis.

Optimization of the sustainable food supply chain with integrative data envelopment analysis approach

ABSTRACT In this study, the supply chain system of the food supply chain (FSC) is assessed. Initially, the operations of farmers are examined using the Data Envelopment Analysis (DEA) technique. Then, locations of relatively efficient farmers are used as potential collection sites for the distribution at the downstream operations. Next, the Multi-Objective Optimization Model is proposed to examine location and distribution channels under the sustainability paradigm. That is, the economic criterion is primarily considered by focusing on minimizing the total supply cost. The social criterion is next deliberated by evaluating the equality aspect of farmers for the fair proportion of vegetable supply. Then, the environmental criterion is incorporated by assessing the CO2 emissions of food transportation activity. Additionally, a trade-off analysis is analyzed to investigate the conflicting behavior of model using the Non-Preemptive Programming method. The sensitivity analysis is further examined by varying the time-requirement parameter to verify the model functionalities. Finally, the regional case study in Thailand based on the food supply data is applied to validate the model. Given that farmers are distant from diverse locations, the results of our study can provide a strategic choice for key decision-makers in the FSC network under sustainability consideration.

Deep learning and big data mining for Metal–Organic frameworks with high performance for simultaneous desulfurization and carbon capture

Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the “one-step” adsorption technology with high-performance metal–organic frameworks (MOFs) was proposed to simultaneously capture the SO2 and CO2. Four machine learning algorithms were used to predict the performance indicators (NCO2+SO2, SCO2+SO2/N2, and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R2 = 0.93). To address sparse data of MOF chemical descriptors, we introduced the Deep Factorization Machines (DeepFM) model, outperforming MLPR with a higher R2 of 0.95. Then, sensitivity analysis was employed to find that the adsorption heat and porosity were the key factors for SO2 and CO2 capture performance of MOF, while the influence of open alkali metal sites also stood out. Furthermore, we established a kinetic model to batch simulate the breakthrough curves of TOP 1000 MOFs to investigate their dynamic adsorption separation performance for SO2/CO2/N2. The TOP 20 MOFs screened by the dynamic performance highly overlap with those screened by the static performance, with 76 % containing open alkali metal sites. This integrated approach of computational screening, machine learning, and dynamic analysis significantly advances the development of efficient MOF adsorbents for flue gas treatment.