Machine Learning Analysis Research Articles

A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Version 0.1.19 of Phenopacket Store includes 6668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

Renewable energy technology innovation and ESG greenwashing: Evidence from supervised machine learning methods using patent text

As global environmental pollution worsens, environmental governance has become a critical aspect of corporate development. In environmental, social, and governance (ESG) risk management, how firms address the threat of greenwashing has emerged as a central focus in achieving sustainable green development. This study explores an under-researched factor contributing to ESG greenwashing: renewable energy technology innovation (RETI). Using supervised machine learning and text analysis methods, the study constructs a proxy variable for RETI and applies it to a sample of Chinese listed companies. The findings reveal that RETI reduces corporate ESG greenwashing, and this effect remains consistent after a series of endogeneity and robustness tests. The inhibitory impact of RETI on ESG greenwashing is more significant when board experiential diversity and media attention are higher. This study contributes to the theoretical basis and demonstration for the research on RETI, greenwashing, managerial experience, and corporate governance.

Machine learning-assisted design of transition metal-doped 2D WSn₂N₄ electrocatalysts for enhanced hydrogen evolution reaction

Hydrogen energy is characterized as environmentally friendly and resourceful. The hydrogen evolution reaction (HER) is a crucial process for hydrogen production and is essential for achieving a transition to sustainable and clean energy. In this work, we conducted a systematic investigation into the hydrogen catalytic activity of two-dimensional WSn2N4 materials. The transition metal atoms (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) were substituted at the N and Sn sites in WSn2N4, respectively. The catalyst's performance was evaluated to verify its catalytic activity for the HER. The results indicate that the Gibbs free energy changes (ΔGH∗) of Ti@W–WSn2N4, V@W–WSn2N4, Fe@W–WSn2N4, Co@W–WSn2N4, and Ni@W–WSn2N4 are close to zero. Among the structures examined, Ti@W–WSn2N4 exhibited the lowest Gibbs free energy change (ΔGH∗ = −0.01 eV), indicating a high degree of catalytic activity. Through machine learning analysis, key features affecting catalytic activity could be directly identified, and a framework for rapid screening was established. This study lays a solid foundation for the design and development of potential HER catalysts in the future.

MRI-Based Multi-Class Relevance Vector Machine Classification of Neurodegenerative Diseases.

Machine learning algorithms are a promising automated candidate that can help mitigate the growing need for dementia experts. Despite the substantial development in MRI-based machine learning analyses, case misclassification is a universal finding, yet the reasons behind misclassification are poorly understood. We implemented a multi-class classification approach that uses relevance vector machine and logistic classification to classify research participants based on their whole-brain T1-weighted MRI scans. A total of 468 participants from seven diagnostic classes were included: 144 healthy controls, 84 Alzheimer's disease, 108 behavioral variant frontotemporal dementia (bvFTD), 30 semantic variant primary progressive aphasia (svPPA), 30 non-fluent variant primary progressive aphasia (nfvPPA), 30 corticobasal syndrome (CBS), and 42 progressive supranuclear palsy syndrome (PSPS). We compared the algorithm's diagnostic accuracy against the clinical, pathological, genetic, and quantitative imaging data. The exact neurodegenerative syndrome was predicted in 71% of the cases, the neurodegenerative disease spectrum was predicted in 80% of the cases, and the algorithm distinguished controls from any dementia in 85% of the cases. The algorithm showed high performance in diagnosing healthy controls, moderate performance in diagnosing AD, bvFTD, and svPPA, and low performance in diagnosing CBS, nfvPPA, and PSPS. Based on the quantitative imaging data, most of the misclassified neurodegenerative cases had minimal atrophy and brain volumes comparable to healthy controls. In AD, early-onset AD cases with minimal brain atrophy represented most of the misclassified cases. In bvFTD, FTD genetic mutation carriers (predominantly C9orf72 repeat expansion), FTD phenocopy, patients meeting only possible bvFTD criteria represented most misclassified cases. Case misclassification in machine learning studies in neurodegenerative diseases results from neurodegenerative disease heterogeneity and the limitations of structural MRI's ability to capture the whole gamut of biological changes. Larger and more inclusive datasets that are representative of population biologic heterogeneity are needed to train better machine learning techniques, and a margin of error is expected and should be acceptable, like the uncertainty of a clinical diagnosis by a dementia expert.

Анализ угроз информационной безопасности органов государственной власти с помощью нейронных сетей

<p>Ensuring the information security of public authorities requires the use of specialized tools that take into account various sources of information threats and their constant changes. This study aims to develop a methodology for analyzing these threats using neural networks. The research uses methods such as machine learning and neural network analysis to systematize the data. The authors adapted the Multi-Layer Perceptron (MLP) architecture and configured the hyperparameters of the neural network to achieve their objectives. The neural network was trained using the Python programming language, and its effectiveness was evaluated using metrics such as accuracy, precision, recall, and F1 score. The results of the study included the development of a method for creating a data set that encompasses assessments of threats to the information security of various public authorities and their sources. Additionally, the study evaluated the effectiveness of neural networks in solving classification problems for public authorities. Finally, the study interpreted the results of neural network analysis to determine the resistance of public authorities against information security threats.</p>

CO2 gas-liquid equilibrium study and machine learning analysis in MEA-DMEA blended amine solutions

The combination of primary and tertiary amines represents a promising approach to improve sorbent performance in CO2 capture by enhancing absorption efficiency and reducing regeneration energy. This study focuses on investigating the absorption performance of binary mixtures of ethanolamine (MEA) and N,N-dimethylethanolamine (DMEA) at temperatures between 298–323 K and CO2 partial pressures between 5–60 kPa. The species generated during the absorption were analyzed using 13C NMR spectroscopy, to clarify the intricate role of MEA and DMEA in the capture process. A developed excess property model for MEA-DMEA, based on excess CO2 loading, predicted equilibrium CO2 solubility data with an average absolute relative deviation (AARD) of 1.6 %. Additionally, the machine learning models XGBoost, RBFNN, and SVR were applied, providing AARD values between 0.86 % and 1.28 %, demonstrating strong agreement between experimental and predicted outcomes. These comprehensive findings enhance our understanding of mixed amines’ mechanisms and practical applications, contributing to ongoing research development.

ML Mental Health Support System: Stress Features Identification with COVID-19 Dataset and Selection Algorithms

The COVID-19 pandemic has caused significant changes in people's lives, resulting in everyone suffering from mental health issues such as stress, financial pressure, depression, frustration, and anxiety. Identifying critical features associated with mental stress can help healthcare professionals to develop effective intervention strategies. This paper aims to design a machine learning-based decision support system (DSS) to assess the mental health status of an individual after COVID-19.The primary objective of this work is to give an in-depth statistical analysis and performance evaluation of machine learning for stress prediction, with the ultimate goal of mitigating the adverse effects of stress on mental health. A survey was carried out on around 1,200 individuals. The research finding shows that age and work area significantly impact mental health. The result analysis was presented for different machine learning approaches in which the Naive Bayes classifier and Logistic Regression achieved the highest accuracy of 99% whereas the Artificial Neural Network (ANN) and Support Vector Machine (SVM) achieved 71% accuracy. Random Forest shows a good performance of 98% and k-Nearest Neighbors (k-NN) shows 75% accuracy. The evaluation results indicate that logistic regression, naive Bayes, and random forest demonstrate superior performance. This research could lead to the development of stress prediction and prevention solutions based on a Decision Support System (DSS).

Leveraging Machine Learning and Image Analysis to Prevent Off - Platform Transactions in Online Marketplaces

Leveraging Machine Learning and Image Analysis to Prevent Off - Platform Transactions in Online Marketplaces

ECBoA-OFS: An Ensemble Classification Model for Botnet Attacks based on Optimal Feature Selection using CPR in IoT

The rapid growth of the Internet of Things (IoT) has indeed introduced new security challenges, and the proliferation of compromised IoT devices has become a significant concern. Botnet attacks, where multiple corrupted devices are managed by a particular object, have become a widespread threat in IoT environments. These are used for a variety of malicious activities, including distributed DDoS attacks, data breaches, and malware distribution. However, detecting IoT botnets poses several challenges due to the resource constraints inherent in many IoT devices. The limitations in computation, storage, and communication capabilities make it challenging to deploy complex ML and deep learning models directly on these devices. This paper proposes an ensemble classification model ECBoA-OFS (Ensemble Classification for Botnet Attack Prediction using Optimal Feature Selection). It focuses on enhancing the accuracy of botnet attack prediction through the integration of ensemble methods and optimal feature selection. It describes a method for optimal feature selection in the context of analyzing the behavior of BoA and malicious traffic flow features in a network using Central Pivot Ranges (CPR). Feature selection is an important step in machine learning and data analysis because it supports to identification of the most important features for a given problem, thereby improving model performance and interpretation. The extracted features are used for model training and ensemble classification for prediction. To evaluate ECBoA-OFS, the N-BaIoT-2021 dataset consisting of regular IoT network traffic and BoA traffic records of corrupted IoT devices is utilized, considering detection precision, sensitivity, specificity, accuracy, and F1-score. Although all ensemble classifier models achieved better detection accuracy through optimal feature selection, the proposed ECBA-OFS shows better results compared to other ensemble classifier results.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BackgroundA better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.MethodsThe current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).ResultsOf 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.ConclusionThese findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.Trial registrationTrial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate

Transparency and accountability are critical components of corporate sustainability. This study uses machine learning and empirical analysis to examine the influence of corporate social responsibility (CSR) committees and environmental, social, and governance (ESG) initiatives on corporate sustainability. Using 2017–2021 Bloomberg Terminal data, we investigated the environmental footprints, disclosure practices, risk profiles, and ESG fund commitments of Fortune 500 companies. Key findings indicate that CSR committees positively impact environmental performance, with an increase in environmental responsibility over time. Policy implications highlight the necessity for collaboration to prioritize environmental sustainability and address climate risk disclosure auditing within the audit profession.

APiS (AI powered intelligence spectrum analyser): hyperspectral imaging machine learning analyser app for mineral processing

ABSTRACT Hyperspectral data, encompassing hundreds of spectral wavelengths, is a powerful tool for mineral processing, as minerals display unique reflection spectra. Machine learning can identify mineral species from hyperspectral data, but its high dimensionality poses challenges for ordinary users. To address this, we developed APiS (AI Powered intelligence Spectrum Analyser), an interactive hyperspectral analysis application requiring no coding. APiS offers a user-friendly interface for data visualisation, learning data generation, advanced machine learning analysis, and model evaluation. This App surpasses previous script-based methods, enabling diverse users in geometallurgy to conduct sophisticated hyperspectral data analysis and fostering creativity in their work.

CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer

Aims: This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression. Background: Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilizes bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients. Objective: Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition. Methods: Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC. Results: The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups. Conclusion: Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.

Structure-function coupling in white matter uncovers the hypoconnectivity in autism spectrum disorder

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder associated with alterations in structural and functional coupling in gray matter. However, despite the detectability and modulation of brain signals in white matter, the structure-function coupling in white matter in autism remains less explored.MethodsIn this study, we investigated structural-functional coupling in white matter (WM) regions, by integrating diffusion tensor data that contain fiber orientation information from WM tracts, with functional connectivity tensor data that reflect local functional anisotropy information. Using functional and diffusion magnetic resonance images, we analyzed a cohort of 89 ASD and 63 typically developing (TD) individuals from the Autism Brain Imaging Data Exchange II (ABIDE-II). Subsequently, the associations between structural-functional coupling in WM regions and ASD severity symptoms assessed by Autism Diagnostic Observation Schedule-2 were examined via supervised machine learning in an independent test cohort of 29 ASD individuals. Furthermore, we also compared the performance of multi-model features (i.e. structural-functional coupling) with single-model features (i.e. functional or structural models alone).ResultsIn the discovery cohort (ABIDE-II), individuals with ASD exhibited widespread reductions in structural-functional coupling in WM regions compared to TD individuals, particularly in commissural tracts (e.g. corpus callosum), association tracts (sagittal stratum), and projection tracts (e.g. internal capsule). Notably, supervised machine learning analysis in the independent test cohort revealed a significant correlation between these alterations in structural-functional coupling and ASD severity scores. Furthermore, compared to single-model features, the integration of multi-model features (i.e., structural-functional coupling) performed best in predicting ASD severity scores.ConclusionThis work provides novel evidence for atypical structural-functional coupling in ASD in white matter regions, further refining our understanding of the critical role of WM networks in the pathophysiology of ASD.

Machine learning analysis/optimization of auxetic performance of a polymeric meta-hybrid structure of re-entrant and meta-trichiral

In the ever-evolving field of materials science, the quest for innovative design solutions continues. One such area of research is the optimization of auxetic performance in meta-hybrid structures. This study delves into the application of machine learning analysis to enhance the properties of a specific meta-hybrid structure combining re-entrant and meta-trichiral designs. The polymeric meta-hybrid structure is subjected to finite element (FE) analysis to determine its Poisson's ratio. Various machine learning methods, such as linear, quadratic, and non-linear approaches, are employed to accurate forecast the Poisson's ratio of the polymeric meta-hybrid structure. Although both linear and quadratic machine learning algorithms have shown some success in predicting Poisson's ratios, they cannot be considered outstanding. As a result, a non-linear machine learning algorithm has been developed to enhance the accuracy of Poisson's ratio prediction. The results indicate that non-linear machine learning techniques out performs both linear and quadratic methods when it came to predicting Poisson's ratio. Specifically, the non-linear model achieved an impressive R-sq value of 93.68%, demonstrating its exceptional accuracy and overall excellence in fitting the data. The optimum conditions for the meta-hybrid structure are determined to be a thickness of 4.35 mm, a temperature of 15 °C, and a strain rate of 1%. Under these specific parameters, the Poisson's ratio of the meta-hybrid structure is measured to be −3.899, well outside isotropic stability limit but acceptable for auxetic structures.

Comprehensive analysis of aging-related gene expression patterns and identification of potential intervention targets.

This study aims to understand the molecular mechanisms underlying the aging process and identify potential interventions to mitigate age-related decline and diseases. This study utilized the GSE168753 dataset to conduct comprehensive differential gene expression analysis and co-expression module analysis. Machine learning and Mendelian randomization analyses were employed to identify core aging-associated genes and potential drug targets. Molecular docking simulations and mediation analysis were also performed to explore potential compounds and mediators involved in the aging process. The analysis identified 4164 differentially expressed genes, with 1893 upregulated and 2271 downregulated genes. Co-expression analysis revealed 21 modules, including both positively and negatively correlated modules between older age and younger age groups. Further exploration identified 509 aging-related genes with distinct biological functions. Machine learning and Mendelian randomization analyses identified eight core genes associated with aging, including DPP9, GNAZ, and RELL2. Molecular docking simulations suggested resveratrol, folic acid, and ethinyl estradiol as potential compounds capable of attenuating aging through modulation of RELL2 expression. Mediation analysis indicated that eosinophil counts and neutrophil count might act as mediators in the causal relationship between genes and aging-related indicators. This comprehensive study provides valuable insights into the molecular mechanisms of aging and offers important implications for the development of anti-aging therapeutics. Key Messages What is already known on this topic - Prior research outlines aging's complexity, necessitating precise molecular targets for intervention. What this study adds - This study identifies novel aging-related genes, potential drug targets, and therapeutic compounds, advancing our understanding of aging mechanisms. How this study might affect research, practice, or policy - Findings may inform targeted therapies for age-related conditions, influencing future research and clinical practices.

Establishment of seven lung ultrasound phenotypes: a retrospective observational study of an LUS registry

BackgroundLung phenotypes have been extensively utilized to assess lung injury and guide precise treatment. However, current phenotypic evaluation methods rely on CT scans and other techniques. Although lung ultrasound (LUS) is widely employed in critically ill patients, there is a lack of comprehensive and systematic identification of LUS phenotypes based on clinical data and assessment of their clinical value.MethodsOur study was based on a retrospective database. A total of 821 patients were included from September 2019 to October 2020. 1902 LUS examinations were performed in this period. Using a dataset of 55 LUS examinations focused on lung injuries, a group of experts developed an algorithm for classifying LUS phenotypes based on clinical practice, expert experience, and lecture review. This algorithm underwent validation and refinement with an additional 140 LUS images, leading to five iterative revisions and the generation of 1902 distinct LUS phenotypes. Subsequently, a validated machine learning algorithm was applied to these phenotypes. To assess the algorithm’s effectiveness, experts manually verified 30% of the phenotypes, confirming its efficacy. Using K-means cluster analysis and expert image selection from the 1902 LUS examinations, we established seven distinct LUS phenotypes. To further explore the diagnostic value of these phenotypes for clinical diagnosis, we investigated their auxiliary diagnostic capabilities.ResultsA total of 1902 LUS phenotypes were tested by randomly selecting 30% to verify the phenotypic accuracy. With the 1902 LUS phenotypes, seven lung ultrasound phenotypes were established through statistical K-means cluster analysis and expert screening. The acute respiratory distress syndrome (ARDS) exhibited gravity-dependent phenotypes, while the cardiogenic pulmonary edema exhibited nongravity phenotypes. The baseline characteristics of the 821 patients included age (66.14 ± 11.76), sex (560/321), heart rate (96.99 ± 23.75), mean arterial pressure (86.5 ± 13.57), Acute Physiology and Chronic Health Evaluation II (APACHE II)score (20.49 ± 8.60), and duration of ICU stay (24.50 ± 26.22); among the 821 patients, 78.8% were cured. In severe pneumonia patients, the gravity-dependent phenotype accounted for 42% of the cases, whereas the nongravity-dependent phenotype constituted 58%. These findings highlight the value of applying different LUS phenotypes in various diagnoses.ConclusionsSeven sets of LUS phenotypes were established through machine learning analysis of retrospective data; these phenotypes could represent the typical characteristics of patients with different types of critical illness.

Understanding temporary residential mobility during urban renewal: Insights from a structured community survey and machine learning analysis

Existing studies on urban renewal have primarily focused on the final effects of urban redevelopment, while often overlooked the social costs incurred during the temporary displacement phase. This gap is significant, as many residents must vacate their homes for an average of 3–5 years during Shantytown redevelopment, which brings about challenges of renting houses and the associated negative impacts on their well-being before returning to their resettled homes. Therefore, this study focuses on examining the temporary residence arising during Shantytown redevelopment while awaiting resettlement. We selected Heze city as our case study area, which has been through China's most intensive redevelopment between 2016 and 2018 that affected about 1.2 million population. A structured community survey was conducted, and 1035 valid samples were collected. We then applied spatiotemporal analysis and the Random Forest model to examine stability, direction, and distance of temporary residence mobility, along with its influencing factors. Findings reveal that 92.4% of households move just once or twice during the temporary phase, indicating the preference for stable residence. Regarding moving direction, households seek life service centers rather than city centers, and prefer familiar community environments. Furthermore, 74.8% of households resettled within 2.5 km of their original residence, indicating a preference for nearby temporary housing. The built environment emerged as the most critical factor influencing the mobility, followed by family socioeconomic status, while housing costs, surprisingly, having the minimal impact. This study highlights the importance of considering the interim social costs in urban renewal projects and provides valuable insights for housing market regulation and urban planning to mitigate these effects.

Machine learning analysis of a large set of homopolymers to predict glass transition temperatures

Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (Tg) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple linear regression (MLR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), gaussian processes for regression (GPR), and multi-layer perceptron (MLP) were explored. As result, a model with the subset of 15 descriptors accurately predicting the glass transition temperatures was developed. The electronic effect indices were determined to be important properties that positively contribute to the Tg values. The SVM-based model showed the best performance with determination coefficients (R2) of 0.813 and 0.770, for training and test sets, respectively. Also, the SVM model showed the lowest estimation error, RMSE = 0.062. In addition, the developed structure-property model was implemented as a web app to be used as an online computational tool to design and evaluate new homopolymers with desired glass transition profiles.

Impact and Effectiveness of COVID-19 Vaccines Based on Machine Learning Analysis of a Time Series: A Population-Based Study.

Background: Although confirmed cases of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been declining since late 2020 due to general vaccination, little research has been performed regarding the impact of vaccines against SARS-CoV-2 in Spain in terms of hospitalizations and deaths. Objective: Our aim was to identify the reduction in severity and mortality of coronavirus disease 2019 (COVID-19) at a nationwide level due to vaccination. Methods: We designed a retrospective, population-based study to define waves of infection and to describe the characteristics of the hospitalized population. We also studied the rollout of vaccination and its relationship with the decline in hospitalizations and deaths. Finally, we developed two mathematical models to estimate non-vaccination scenarios using machine learning modeling (with the ElasticNet and RandomForest algorithms). The vaccination and non-vaccination scenarios were eventually compared to estimate the number of averted hospitalizations and deaths. Results: In total, 498,789 patients were included, with a global mortality of 14.3%. We identified six waves or epidemic outbreaks during the observed period. We established a strong relationship between the beginning of vaccination and the decline in both hospitalizations and deaths due to COVID-19 in all age groups. We also estimated that vaccination prevented 170,959 hospitalizations (CI 95% 77,844-264,075) and 24,546 deaths (CI 95% 2548-46,543) in Spain between March 2021 and December 2021. We estimated a global reduction of 9.19% in total deaths during the first year of COVID-19 vaccination. Conclusions: Demographic and clinical profiles changed over the first months of the pandemic. In Spain, patients over 80 years old and other age groups obtained clinical benefit from early vaccination. The severity of COVID-19, in terms of hospitalizations and deaths, decreased due to vaccination. Our use of machine learning models provided a detailed estimation of the averted burden of the pandemic, demonstrating the effectiveness of vaccination at a population-wide level.