Validation Data Research Articles

High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model

The state of health (SOH) of lithium-ion batteries plays a crucial role in maintaining the stability of electric vehicle systems. To address the issue of low accuracy in existing prediction models, this article introduces an enhanced grey wolf algorithm for optimizing the hybrid kernel least squares support vector regression machine used in lithium-ion battery SOH prediction. This research extracted four key health features from the raw data of each battery in the Cycle dataset, which is publicly accessible. Data preprocessing of health features involved Pearson correlation analysis and Hampel filtering techniques. The framework of least squares support vector regression constructs a hybrid kernel function of polynomial kernel function and radial basis function. The integration of differential evolution and the law of survival of the fittest into the grey wolf algorithm enhances its optimization ability. The improved grey wolf algorithm optimizes the parameters of the hybrid kernel least squares support vector regression machine, improving the accuracy and robustness of the model. After data validation, it is known that the optimal average absolute error value predicted by the model can reach 0.32 %. This indicates that the proposed method is effective and feasible.

The Use of Islamic Attribute in Food Product Marketing: Expert Views

The vast number of Muslim consumers in Malaysia, which accounts for 63.5% of the total Malaysian population, offers a vast market segment to food producers—the food producers employ various marketing strategies to compete in the market. Among the strategies employed is the use of Islamic attributes in marketing activities. Food product marketers used Islamic attributes such as Muslim names, pictures, emblems, Quranic verses, and Hadith in their marketing campaigns. The free use of the Islamic attributes has drawn attention to knowing the Islamic perspective of the use. Through in-depth interviews, this research paper aimed to reveal the views of 7 Shariah experts in a rich and contextualized account of using Islamic attributes in food product marketing. The experts were selected through purposive and snowball sampling methods. Thematic analysis was used to analyze the data, which resulted in the emergence of six themes in this study. These themes include (1) protection of Islamic honor and dignity, (2) prohibition of using Islam for personal gain, (3) prohibition of unjust practices, (4) prohibition of superstitious or syirik acts, (5) promotion of good business practices, and (6) promotion of a healthy diet, revealing the Islamic perspective on its use. This study contributes knowledge to Islamic marketing and the rulings on using Islamic attributes according to the Shariah principle. This research sheds light on the ethical implications of using Islamic attributes in scholarly and marketing domains. Further, this research explores the triangulation method of data analysis and validity via Shariah and socio-research.

Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout

A good way to spread knowledge about Palembang songket woven cloth patterns is to use information technology, especially artificial intelligence technology. This study's main goal is to develop a ResNet model with dropout regularization methods and find out how dropout regularization affects the ResNet model for detecting Palembang songket fabric motif with more data. Data was collected in places like tujuh saudara songket, Zainal songket, songket PaSH, AMS songket, and batik, Ernawati songket, Nabilah collections, Ilham songket, and Marissa songket. We used eight class of data for this research. A dataset of 7,680 data for training, 960 data for validation, and 960 data for testing is a dataset that has been prepared to be implemented in experiments. In the final results, the experimental results for DResNet demonstrated that accuracy at the training stage was 92.16%, accuracy at the validation stage was 78.60%, and accuracy at the submission stage was 80.3%. The experimental results also show that dropouts are able to increase the accuracy of the ResNet model by adding +1.10% accuracy in the training process, adding +1.80% accuracy in the validation process, and adding +0.40% accuracy in the testing process.

Comparison of Analgesic Effects of Continuous Femoral Nerve Block, Femoral Triangle Block, and Adductor Block After Total Knee Arthroplasty: A Randomized Clinical Trial.

This study aimed to compare the analgesic effects of continuous femoral nerve block (FNB), femoral triangle block (FTB), and adductor canal block (ACB) following total knee arthroplasty (TKA). The goal was to identify the most effective nerve block technique among these. Patients undergoing TKA were randomly assigned to 1 of 3 groups: FNB, FTB, or ACB. Nerve blocks were administered preoperatively, with catheters placed for patient-controlled nerve analgesia (PCNA). The primary end point was the Numeric Rating Scale (NRS) score at movement at 24 hours postsurgery. Secondary end points included NRS scores at rest and movement, quadriceps strength, Timed Up and Go (TUG) test performance, range of motion, effective PCNA utilization, and opioid consumption at various postsurgery time points. Of the 94 valid data sets analyzed (FNB: 31, FTB: 31, ACB: 32), significant differences were observed in the primary end point (H=7.003, P =0.03). Post hoc analysis with Bonferroni correction showed that the FNB group had a significantly lower median pain score (3 [2 to 4]) compared with the ACB group (4 [3 to 5], Bonferroni-adjusted P =0.03). Regarding secondary end points, both the FNB and FTB groups had significantly lower NRS scores than the ACB group at various time points after surgery. Quadriceps strength and TUG completion were better in the FTB and ACB groups. There were no statistically significant differences among the groups for the other end points. Continuous FTB provides postoperative analgesia comparable to FNB but with the advantage of significantly less impact on quadriceps muscle strength, a benefit not seen with FNB. Both FTB and ACB are effective in preserving quadriceps strength postoperatively.

Leaf Disease Detection Using Convolutional Neural Network

Leaf Disease Detection Using Convolutional Neural Network

Blockchian Based Hybrid Consensus Algorithm for Data Security in Edge Computing

Abstract: Edge computing has emerged as a paradigm to process data closer to its source, enabling low-latency and real-time applications. However, the distributed and heterogeneous nature of edge environments poses significant challenges for ensuring data security and integrity. Blockchain technology offers a promising solution by providing a decentralized and immutable ledger for recording transactions. This abstract proposes a consensus algorithm tailored for securing data in edge computing environments using blockchain technology. The proposed consensus algorithm leverages a hybrid approach combining Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT). PoA ensures that only trusted nodes within the edge network are eligible to participate in the consensus process, mitigating the risk of malicious attacks. PBFT enhances the efficiency and scalability of the consensus algorithm by allowing fast decision-making among a subset of nodes, thereby reducing latency in data validation and verification. Furthermore, smart contracts are employed to enforce data access control and execute predefined rules for data processing and sharing. These smart contracts facilitate secure and transparent interactions among edge devices, ensuring that only authorized parties can access and manipulate sensitive data. The integration of blockchain technology with edge computing offers several benefits, including enhanced data security, traceability, and accountability. By employing a tailored consensus algorithm, edge computing environments can effectively mitigate security threats while maintaining low-latency data processing capabilities. Future research directions may explore optimization techniques to further improve the performance and scalability of the proposed solution in large-scale edge networks.

Analisis Manajemen Pengangkutan TBS di PT. Serba Huta Jaya Desa Sumber Mulyo, Kecamatan Marbau, Kabupaten Labuhanbatu Utara, Sumatera Utara

This research aims to determine the management of transporting fresh palm fruit bunches (FFB) at PT. Serba Huta Jaya, analyzed the performance of transporting FFB from the harvesting site (TPH) to the palm oil mill (PKS), analyzing the obstacles in transporting FFB from TPH to PKS. The research method used in this research is mixed methods. According to Sugiyono (2015), mixed research methods are a research method between quantitative methods and qualitative methods to be used together in a research activity, so that more comprehensive, valid, reliable and objective data is obtained. The sample in this study was 10 FFB transport drivers and 20 loading and unloading workers. The results of this research indicate that FFB transportation management at PT. Serba Huta Jaya is still not doing well because there are frequent delays in FFB deliveries resulting in fruit being left on the trucks. There are several things that must be considered, namely road access for transporting FFB, the speed of harvest employees in cutting fruit, and damage to trucks in the field. The average performance of transporting FFB from TPH to PKS is 2.08 tonnes/hour and the average cost for one transport is Rp. 62.96/kg. The obstacles faced when transporting fruit from TPH to PKS were that the roads were still damaged at several points and the trucks were damaged while in the field.

Meta-analysis of new intervention measures for preventing side effects of artificial joint replacement

BACKGROUND Intertrochanteric fracture of the femur occurs mostly among older people, and seriously affects daily life and quality of life. At present, physical intervention, drug treatment, routine intervention and rehabilitation training are widely used for prevention of side effects, but it is still inconclusive which intervention has the best effect. AIM To compare the effects of new intervention measures for preventing side effects of artificial joint replacement. METHODS We searched the Chinese and English literatures for comparative studies on the prevention of side effects of new interventions for artificial joint replacement from July 2013 to June 2023 in China HowNet, PubMed, Wanfang, Weipu and other databases. Study quality was evaluated by improved Jadad scoring standard, and the effects of different interventions on preventing different complications were analyzed by meta-analysis of evidence-based medicine with Review Manager 5.0 software. RESULTS Ten articles, including 869 cases, were finally included. The preventive effects of different interventions on the side effects of artificial joint replacement were studied, and valid data were extracted. There were two articles on the preventive effects of drug intervention, four on comparison of the preventive effects of combined and single interventions, and three on the preventive effects of physical intervention, rehabilitation training and routine intervention. Meta-analysis showed that the preventive effect of rivaroxaban was significantly better than low molecular weight heparin calcium [mean difference (MD) = -0.16, 95%CI: -0.28 to -0.04, P < 0.05]. The effect of combined intervention was significantly better than that of single intervention (MD = -0.08, 95%CI: -0.16 to -0.01, P < 0.001). Physical intervention was significantly better than routine intervention and rehabilitation training (MD = 0.26, 95%CI: 0.16–0.36, P < 0.001). CONCLUSION Rivaroxaban combined with rehabilitation training is preferred for preventing deep vein thrombosis after artificial joint replacement. In the prevention of pulmonary embolism, rivaroxaban drug intervention is given priority. The effect of combined intervention is better than that of single intervention.

Optimizing Productive Waqf Management to Achieve Sustainable Development Goal's (Study of the Indonesian Waqf Board (BWI) Representative of Lampung Province)

This research involves field research using a descriptive qualitative approach. The data collection techniques include observation, interviews, and documentation. Subsequently, data analysis was conducted through stages of data collection, data reduction, data display, and conclusion drawing. Data validity is ensured through source triangulation and technical triangulation. The results showed that the productive waqf managed by BWI Lampung Province is in the form of cash waqf. The management of cash waqf is carried out through three stages: collection, management, and distribution. BWI’s steps in optimizing productive waqf management involve improving BWI’s management, increasing public education about cash waqf, enhancing the use of digital technology, fostering cooperation with various stakeholders, and enhancing the competence of the nazir. Additionally, the contribution of BWI Lampung Province to the SDGs is evident in the achievement of three goals: no hunger, quality education, and decent work and economic growth. Keywords: productive Waqf, sustainable development goal’s, Indonesian Waqf Board

Effects of Modified Schema-Based Instruction to Teach Students With Autism to Solve Additive Compare Problems

This study assessed the effectiveness of modified schema-based instruction (MSBI) in teaching students with autism spectrum disorder (ASD) how to solve additive compare word problems. The study was conducted in Spain, and it involved three students with ASD aged 6, 7, and 9. A single-case, multiple-baseline across-students design was followed. Results indicated a functional relation between MSBI and improved performance in solving additive compare problems of type difference amount unknown and compare amount unknown, maintained for 4 to 6 weeks post-intervention. Additionally, two students successfully applied their skills to reference-amount-unknown compare problems, demonstrating successful generalization. However, their generalization to problems involving two operations was somewhat limited by difficulties with one of the operations. Social validity data collected from the students’ families and teachers indicated that both groups had positive views on the use of the MSBI for mathematical word problem-solving skills.

Development and first-stage validation of a digital version of the Digit Symbol Substitution test for use in assessing cognitive function in older people with diabetes.

To describe the development and report the first-stage validation of a digital version of the digit symbol substitution test (DSST), for assessment of cognitive function in older people with diabetes. A multidisciplinary team of experts was convened to conceptualize and build a digital version of the DSST and develop a machine-learning (ML) algorithm to analyse the inputs. One hundred individuals with type 2 diabetes (aged ≥ 60 years) were invited to participate in a one-time meeting in which both the digital and the pencil-and-paper (P&P) versions of the DSST were administered. Information pertaining to demographics, laboratory measurements, and diabetes indices was collected. The correlation between the digital and P&P versions of the test was determined. Additionally, as part of the validation process, the performance of the digital version in people with and without known risk factors for cognitive impairment was analysed. The ML model yielded an overall accuracy of 89.1%. A strong correlation was found between the P&P and digital versions (r = 0.76, p < 0.001) of the DSST, as well as between the ML model and the manual reading of the digital DSST (r = 0.99, p < 0.001). This study describes the development of and provides first-stage validation data for a newly developed digital cognitive assessment tool that may be used for screening and surveillance of cognitive function in older people with diabetes. More studies are needed to further validate this tool, especially when self-administered and in different clinical settings.

Multitemporal Field-Based Maize Plant Height Information Extraction and Verification Using Solid-State LiDAR

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland.

Critical Analysis of Discipline Character Building through Discipline Week Activities for Students of Muhammadiyah 4 Banjarnegara Elementary School

This study aims to analyze the formation of the disciplinary character of grade 4 and 5 students at SD Muhammadiyah Banjarnegara. One way to shape the character of discipline is by carrying out Discipline Week activities, a collaboration between the school and Kodim 0704 Banjarnegara. With the implementation of the program, it is expected that the discipline of students will increase significantly. Discipline is an important attitude that affects the success of students both in the present and the future. This research uses the qualitative descriptive method of case study type. Data collection techniques are carried out by means of interviews, observation and documentation. In this case, researchers use data validity tests using triangulation techniques. The results showed that the activities of Discipline Week were carried out well and effectively. This activity can motivate students to be disciplined in time and behavior.

Assessing the utility of Munsell soil color in building and evaluating spectral models for soil clay content prediction

AbstractThe present study examined how the use of soil color can help build and evaluate clay content prediction models from laboratory visible and near infrared spectroscopic data. This study was based on a regional database containing 449 soil samples collected over Karnataka state in India, which has been divided into red soils (240 samples) and black soils (209 samples) based on their Munsell soil color. Partial least squares regression models were calibrated and validated from both the regional datasets and subsets stratified as red and black soils. In addition, a random forest model was used to classify the validation soil samples into black and red classes to evaluate models’ performance. First, while the clay content predicted by the regression model built from regional data was evaluated as correct at regional scale (R2val of 0.75), this model was evaluated as more accurate over black (R2val of 0.8) than red (R2val of 0.63) soil samples. Second, the regression models built from subsets stratified per soil color provided different performances than the regression model built from the regional data, both at the regional scale and soil color scale. In conclusion, this study demonstrated that (1) predictions are highly dependent on calibration data, (2) the interpretation of prediction performances relies heavily on validation data, and (3) pedological knowledge, such as soil color, can be effectively employed as an encouraging covariate in both the construction and evaluation of regression models.

Clinical biomarker-based biological age predicts deaths in Brazilian adults: the ELSA-Brasil study.

Biological age is a construct that seeks to evaluate the biological wear and tear process of the organism that cannot be observed by chronological age. We estimate individuals' biological age based on biomarkers from multiple systems and validate it through its association with mortality from natural causes. Biological age was estimated in 12,109 participants (6621 women and 5488 men) from the first visit of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) who had valid data for the biomarkers used in the analyses. Biological age was estimated using the Klemera and Doubal method. The difference between chronological age and biological age (Δage) was computed. Cox proportional hazard models stratified by sex were used to assess whether Δage was associated with mortality risk after a median follow-up of 9.1years. The accuracy of the models was estimated by the area under the curve (AUC). Δage had equal mean for men and women, with greater variability for men. Cox models showed that every 1-year increase in Δage was associated with increased mortality in men (HR (95% CI) 1.21; 1.17-1.25) and women (HR (95% CI) 1.24; 1.15-1.34), independently of chronological age. Results of the AUC demonstrated that the predictive power of models that only included chronological age (AUC chronological age = 0.7396) or Δage (AUC Δage = 0.6842) was lower than those that included both, chronological age and Δage (AUC chronological age + Δage = 0.802), in men. This difference was not observed in women. We demonstrate that biological age is strongly related to mortality in both genders and is a valid predictor of death in Brazilian adults, especially among men.

FAIR health data in the national and international data space

Health data are extremely important in today's data-driven world. Through automation, healthcare processes can be optimized, and clinical decisions can be supported. For any reuse of data, the quality, validity, and trustworthiness of data are essential, and it is the only way to guarantee that data can be reused sensibly. Specific requirements for the description and coding of reusable data are defined in the FAIR guiding principles for data stewardship. Various national research associations and infrastructure projects in the German healthcare sector have already clearly positioned themselves on the FAIR principles: both the infrastructures of the Medical Informatics Initiative and the University Medicine Network operate explicitly on the basis of the FAIR principles, as do the National Research Data Infrastructure for Personal Health Data and the German Center for Diabetes Research.To ensure that aresource complies with the FAIR principles, the degree of FAIRness should first be determined (so-called FAIR assessment), followed by the prioritization for improvement steps (so-called FAIRification). Since 2016, aset of tools and guidelines have been developed for both steps, based on the different, domain-specific interpretations of the FAIR principles.Neighboring European countries have also invested in the development of anational framework for semantic interoperability in the context of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Concepts for comprehensive data enrichment were developed to simplify data analysis, for example, in the European Health Data Space or via the Observational Health Data Sciences and Informatics network. With the support of the European Open Science Cloud, among others, structured FAIRification measures have already been taken for German health datasets.

Diagnostic utility of clinicodemographic, biochemical and metabolite variables to identify viable pregnancies in a symptomatic cohort during early gestation

A significant number of pregnancies are lost in the first trimester and 1–2% are ectopic pregnancies (EPs). Early pregnancy loss in general can cause significant morbidity with bleeding or infection, while EPs are the leading cause of maternal mortality in the first trimester. Symptoms of pregnancy loss and EP are very similar (including pain and bleeding); however, these symptoms are also common in live normally sited pregnancies (LNSP). To date, no biomarkers have been identified to differentiate LNSP from pregnancies that will not progress beyond early gestation (non-viable or EPs), defined together as combined adverse outcomes (CAO). In this study, we present a novel machine learning pipeline to create prediction models that identify a composite biomarker to differentiate LNSP from CAO in symptomatic women. This prospective cohort study included 370 participants. A single blood sample was prospectively collected from participants on first emergency presentation prior to final clinical diagnosis of pregnancy outcome: LNSP, miscarriage, pregnancy of unknown location (PUL) or tubal EP (tEP). Miscarriage, PUL and tEP were grouped together into a CAO group. Human chorionic gonadotrophin β (β-hCG) and progesterone concentrations were measured in plasma. Serum samples were subjected to untargeted metabolomic profiling. The cohort was randomly split into train and validation data sets, with the train data set subjected to variable selection. Nine metabolite signals were identified as key discriminators of LNSP versus CAO. Random forest models were constructed using stable metabolite signals alone, or in combination with plasma hormone concentrations and demographic data. When comparing LNSP with CAO, a model with stable metabolite signals only demonstrated a modest predictive accuracy (0.68), which was comparable to a model of β-hCG and progesterone (0.71). The best model for LNSP prediction comprised stable metabolite signals and hormone concentrations (accuracy = 0.79). In conclusion, serum metabolite levels and biochemical markers from a single blood sample possess modest predictive utility in differentiating LNSP from CAO pregnancies upon first presentation, which is improved by variable selection and combination using machine learning. A diagnostic test to confirm LNSP and thus exclude pregnancies affecting maternal morbidity and potentially life-threatening outcomes would be invaluable in emergency situations.

Deconvolution analysis identified altered hepatic cell landscape in primary sclerosing cholangitis and primary biliary cholangitis

IntroductionPrimary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are characterized by ductular reaction, hepatic inflammation, and liver fibrosis. Hepatic cells are heterogeneous, and functional roles of different hepatic cell phenotypes are still not defined in the pathophysiology of cholangiopathies. Cell deconvolution analysis estimates cell fractions of different cell phenotypes in bulk transcriptome data, and CIBERSORTx is a powerful deconvolution method to estimate cell composition in microarray data. CIBERSORTx performs estimation based on the reference file, which is referred to as signature matrix, and allows users to create custom signature matrix to identify specific phenotypes. In the current study, we created two custom signature matrices using two single cell RNA sequencing data of hepatic cells and performed deconvolution for bulk microarray data of liver tissues including PSC and PBC patients.MethodsCustom signature matrix files were created using single-cell RNA sequencing data downloaded from GSE185477 and GSE115469. Custom signature matrices were validated for their deconvolution performance using validation data sets. Cell composition of each hepatic cell phenotype in the liver, which was identified in custom signature matrices, was calculated by CIBERSORTx and bulk RNA sequencing data of GSE159676. Deconvolution results were validated by analyzing marker expression for the cell phenotype in GSE159676 data.ResultsCIBERSORTx and custom signature matrices showed comprehensive performance in estimation of population of various hepatic cell phenotypes. We identified increased population of large cholangiocytes in PSC and PBC livers, which is in agreement with previous studies referred to as ductular reaction, supporting the effectiveness and reliability of deconvolution analysis in this study. Interestingly, we identified decreased population of small cholangiocytes, periportal hepatocytes, and interzonal hepatocytes in PSC and PBC liver tissues compared to healthy livers.DiscussionAlthough further studies are required to elucidate the roles of these hepatic cell phenotypes in cholestatic liver injury, our approach provides important implications that cell functions may differ depending on phenotypes, even in the same cell type during liver injury. Deconvolution analysis using CIBERSORTx could provide a novel approach for studies of specific hepatic cell phenotypes in liver diseases.

Blockchain self-update smart contract for supply chain traceability with data validation

Abstract A sustainable supply chain management strategy reduces risks and meets environmental, economic and social objectives by integrating environmental and financial practices. In an ever-changing environment, supply chains have become vulnerable at many levels. In a global supply chain, carefully tracing a product is of great importance to avoid future problems. This paper describes a self-updating smart contract, which includes data validation, for tracing global supply chains using blockchains. Our proposal uses a machine learning model to detect anomalies on traceable data, which helps supply chain operators detect anomalous behavior at any point in the chain in real time. Hyperledger Caliper has been used to evaluate our proposal, and obtained a combined average throughput of 184 transactions per second and an average latency of 0.41 seconds, ensuring that our proposal does not negatively impact supply chain processes while improving supply chain management through data anomaly detection.

Analysis of AlphaMissense data in different protein groups and structural context

Single amino acid substitutions can profoundly affect protein folding, dynamics, and function. The ability to discern between benign and pathogenic substitutions is pivotal for therapeutic interventions and research directions. Given the limitations in experimental examination of these variants, AlphaMissense has emerged as a promising predictor of the pathogenicity of missense variants. Since heterogenous performance on different types of proteins can be expected, we assessed the efficacy of AlphaMissense across several protein groups (e.g. soluble, transmembrane, and mitochondrial proteins) and regions (e.g. intramembrane, membrane interacting, and high confidence AlphaFold segments) using ClinVar data for validation. Our comprehensive evaluation showed that AlphaMissense delivers outstanding performance, with MCC scores predominantly between 0.6 and 0.74. We observed low performance on disordered datasets and ClinVar data related to the CFTR ABC protein. However, a superior performance was shown when benchmarked against the high quality CFTR2 database. Our results with CFTR emphasizes AlphaMissense’s potential in pinpointing functional hot spots, with its performance likely surpassing benchmarks calculated from ClinVar and ProteinGym datasets.