Effective Tool For Prediction Research Articles

Extreme learning machine for soil temperature prediction using only air temperature as input.

Soil temperature (TS) is a crucial parameter in many fields, especially agriculture. In developing countries like Algeria, the soil temperatures (TS) and the meteorological data are limited. This study investigates the use of Extreme Learning Machine (ELM) for the accurate prediction of daily ST at three different depths (30cm, 60cm, and 100cm) using a minimal number of climatic inputs. The inputs used in this study include maximum and minimum air temperatures, relative humidity, and day of the year (DOY) as a representative of the temporal component. Five different combinations of inputs were used to develop ELM models and determine the best set of input variables. The ELM models were then compared with traditional methods such as multiple linear regression, artificial neural networks, and adaptive neuro-fuzzy inference system. Based on evaluation metrics such as R, RMSE, and MAPE, the ELM models with air temperatures and DOY as inputs (ELM-M0 and ELM-M3) demonstrated superior performance at all depths when compared to the other techniques. The most accurate predictions were found at a depth of 100cm using the ELM-M3 model, which employed inputs of minimum and maximum air temperatures and DOY, with R value of 0.98, RMSE of 0.68°C, and MAPE of 3.4%. The results demonstrate that the inclusion of DOY in the climatic dataset significantly enhances the performance and accuracy of machine learning models for ST prediction. The ELM was found to be a fast, simple, effective, and useful tool for TS prediction.

Development and validation of competitive risk model for older women with metaplastic breast cancer

BackgroundMetaplastic breast cancer (MpBC) is a rare histological subtype of breast cancer. This study aims to establish a competitive risk model for older women with MpBC to predict patients’ survival accurately.MethodsData on patients diagnosed with MpBC from 2010 to 2019 are from the Surveillance, Epidemiology and End Results (SEER) program in the United States. All patients were randomly assigned to the training set and validation set. The proportional sub-distribution risk model was used in the training set to analyze the risk factors affecting patient death. Based on the risk factors for cancer-specific mortality (CSM) in patients, we constructed a competitive risk model to predict patients’ 1-, 3-, and 5-year cancer-specific survival. Then we used the concordance index (C-index), the calibration curve and the area under the receiver operating characteristic curve (AUC) to validate the discrimination and accuracy of the model.ResultsOne thousand, four hundred twelve older women with MpBC were included in this study. Age, T stage, N stage, M stage, tumor size, surgery and radiotherapy were risk factors for CSM. We established a competitive risk model to predict 1-, 3-, and 5-year cancer-specific survival in older women with MpBC. The C-index of the model was 0.792 in the training set and 0.744 in the validation set. The calibration curves in the training and validation sets showed that the model’s predicted values were almost consistent with the actual observed values. The AUC results show that the prediction model has good accuracy.ConclusionWe developed a competitive risk model based on these risk factors to predict cancer-specific survival in older women with MpBC. The validation results of the model show that it is a very effective and reliable prediction tool. This predictive tool allows doctors and patients to make individualized clinical decisions.

Carbon price prediction based on LsOALEO feature selection and time-delay least angle regression

Accurate carbon price prediction can help governments to improve the environment and reduce carbon emissions at a lower cost. To improve the prediction accuracy of carbon price, many scholars have attempted to use multiple factors to predict the carbon price. This method improves carbon price prediction, but also brings problems such as increased complexity and decreased interpretability of prediction models. When analyzing the influencing factors of carbon price, scholars tend to overlook the coupling relationship between feature selection and time delay, resulting in redundant or missing influencing factors. To solve the above problems, we innovatively propose a carbon price prediction model based on LsOALEO feature selection and time-delay least angle regression (LsOALEO-FSTDLARS). The model is mainly composed of two parts: (1) Based on LARS, the prediction model is constructed by integrating feature selection and time delay, and the feasibility of the model is proved theoretically. (2) On the basis of lioness optimization algorithm, the local escape operator is used to improve the global search ability of lioness optimization algorithm, and the algorithm is used for feature selection and time delay optimization. Finally, we conduct experiments on the data sets of the carbon markets in Guangdong, Shanghai, Hubei, Tianjin and China, and compares the proposed method with various other prediction methods. The results of empirical analysis show that compared with the best performing single and combined forecasting methods, LsOALEO-FSTDLARS has an average reduction of 31.05% and 1.30% in RMSE, 46.62% and 5.37% in MAPE, and an average increase of 31.39% and 0.94% in R2 respectively. All of the above indicate that the LsOALEO-FSTDLARS model has higher prediction accuracy, stronger generalization ability and more stable effect, and can be used as an effective tool for carbon price prediction.

A decision tree model for accurate prediction of sand erosion in elbow geometry

Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0%, 6.27%, 6.26%, and 5.5%, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industry's cost and time.

Influence of genetic polymorphisms on serum biomarkers of cardiac health.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide which is why early risk prediction is crucial. Discrete Polygenic risk score (PRS) measurement using saliva or dried blood spot samples collected at home poses a convenient means for early CVD risk assessment. The present study assessed the effects of 28 disease-associated single nucleotide polymorphisms (SNPs) on 16 serological cardiac markers and also aggregated the risk alleles into a PRS to evaluate its applicability in CVD-risk prediction. The study assessed genetic and serological markers in 184 individuals. The association between serological markers and individual genetic variants was evaluated using a two-tailed t test while the associations of serum markers with the PRS was analyzed using the Pearson correlation. The comparative analysis of genotypes revealed statistically significant associations between serum markers and CVD-associated SNPs with Apo B: Apo A-1, LDL Direct, Apo B, sdLDL, hsCRP, Lp(a), NT-proBNP, and PLAC levels being significantly associated with the risk alleles of the SNPs, rs12526453, rs5186, rs10911021, rs1801131, rs670, rs10757274, and rs10757278. Increased PLAC levels were associated with rs10757274 and rs10757278 (P < .05). The SNPs, rs1801133, rs1549758, rs1799983, rs5082, and rs5186 were significantly associated with an increase in the cardioprotective markers, HDL and ApoA1 (P < .05). Furthermore, the PRS was associated with increasing levels of several serum cardiac markers (r2 > 0.6). Significant correlations were observed between high PRSs and NT-proBNP and ox-LDL levels with the r2 values being 0.82 (95% CI = 0.13-0.99; P = .03) and 0.94 (95% CI = 0.63-0.99; P = .005), respectively. The present study reports that SNPs have differential effects on serum markers with rs12526453, rs5186, rs10911021, rs1801131, rs670, rs10757274, and rs10757278 showing significant associations with elevated marker levels, which are indicators of deteriorating cardiac health. A unified PRS using several SNPs was also associated with an increase in serum markers levels, especially, NT-proBNP and ox-LDL. Genetic assessment via a convenient at-home collection to calculate the PRS can serve as an effective predictive tool for early CVD-risk assessment. This may help identify the risk groups that may require increased serological monitoring.

Ferric Ions Modulated Carbon Dots as a Fluorescent Probe for the Detection of Neopterin: An Inflammatory Prognostic Marker for Risk Prediction and Severity of Covid‐19

AbstractNeopterin (NPT) is a Guanosine Tri Phosphate (GTP) metabolite produced by activated macrophages and is considered a pro‐inflammatory and atherosclerotic agent. Beyond this NPT is a multi‐utility biomarker for assessing the status of various diseases including cardiovascular disease, depression, and malignant tumors as well as infections including viral and bacterial origin. Recent clinical investigations suggest the use NPT as a prognostic marker for the early prediction of the severity of Covid‐19, which is the prime concern of global public health. One of the main immunopathological characteristics of Covid‐19 is an excessive inflammatory response, and the severity of the infection is linked with macrophage activation syndrome (MAS). Hence the assessment of neopterin serves as an effective diagnostic tool for the risk prediction of Covid‐19. We present a fluorescence turn‐on sensor for neopterin using ferric ion‐modulated sulfur and nitrogen co‐doped carbon quantum dots. The linear detection of NPT was achieved with a limit of detection of 3.11 nM.

Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming

This article aims to gain insights into the prevailing public sentiment during the policy relaxation period by examining whether the post-COVID-19 landscape reflects signs of withering or booming conditions. Employing methods from natural language processing (NLP) and machine learning (ML), the analysis reveals a predominance of positive sentiment from December 7, 2022 to May 17, 2023, indicative of an optimistic perspective and a potentially flourishing environment. A predictive model based on logistic regression emerges as a notably effective tool for sentiment prediction, suggesting potential utility in predicting future public health crises. A comparison of sentiments in translations by the government aligns with previous research, revealing a less favorable depiction of translated texts compared to the source texts. Furthermore, the commonality index, a measure of group consensus value, surpasses the typical range, while the certainty index, a measure of confidence, slightly falls below the norm. These findings offer valuable insights for policy considerations while highlighting areas for international communication and understanding improvement.

The International Society for Biological and Environmental Repositories Presents Abstracts from Its Annual Meeting

The International Society for Biological and Environmental Repositories Presents Abstracts from Its Annual Meeting

Development and validation of a nomogram model to predict primary graft dysfunction in patients after lung transplantation based on the clinical factors.

Primary graft dysfunction (PGD), a significant complication that can affect patients' prognosis and quality of life, develops within 72 h post lung transplantation (LTx). Early detection and prevention of PGD should be given special consideration. The purpose of this study was to create a clinical prediction model to forecast the occurrence of PGD. We collected information on 622 LTx patients from Wuxi People's Hospital from 2016 to 2020 and used the data to construct the prediction model. Information on 224 patients from 2021 to June 2022 was used for external validation. We used LASSO regression for variable screening. A nomogram was developed for model presentation. Distinctness, fit, and calibration were used to evaluate the performance of the model. Subjects with respiratory failure, who received fresh frozen plasma, donor age, donor gender, donor mechanism of death, donor smoking, donor ventilator use time, and donor PaO 2/FiO 2 ratio were independent predictor variables for the occurrence of PGD. The area under the curve of the nomogram was .779. The Hosmer-Lemeshow test showed a good model fit (P= .158). The calibration curve of the nomogram is fairly close to the ideal diagonal. Moreover, the decision curve analysis revealed a positive net benefit of the model. External validation also confirmed the reliability of the model. The nomogram of PGD based on clinical risk factors in postoperative LTx patients was established with high reliability. It provides clinicians and nurses with a new and effective tool for early prediction of PGD and early intervention.

A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.

Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.

Preoperative cerebrospinal fluid biomarkers may be associated with postoperative delirium in patients undergoing knee/hip arthroplasty: the PNDABLE study

BackgroundIn the global aging population, the incidence of postoperative delirium (POD) is increasing. Therefore, finding its effective predictive tools becomes crucial. We aimed to identify potential Cerebrospinal fluid (CSF)biomarkers for POD.MethodsA total of 825 patients undergoing knee/hip arthroplasty under combined spinal-epidural anesthesia were selected. The patients were aged 40 to 90 years with American Society of Anesthesiologists physical status I~II. The Mini-Mental State Examination was completed 1 day before the operation. CSF was extracted after successful spinal-epidural combined puncture, and α-synuclein (α-syn), amyloid beta40 (Aβ40), amyloid beta42 (Aβ42), t-Tau, phosphorylated Tau (p-Tau), progranulin (PGRN) and soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in the CSF were measured by enzyme-linked immunosorbent assays (ELISA). The patient’s operation time, anesthesia time, intraoperative blood loss and fluid input were also recorded. After the operation, the occurrence rate and severity of POD were determined by the Confusion Assessment Method and the Memorial Delirium Assessment Scale (MDAS), respectively. Patients were categorized into POD group and non-POD group. Logistic regression analysis was performed on the indicators with statistically significant differences, and the area under the ROC curve (AUC) was used to estimate the predictive accuracy of the biomarkers for POD.ResultsA total of 92 patients developed POD and the incidence of POD was 11.15%. The results of the multivariable logistic regression showed that CSF t-Tau (P = 0.004, OR = 1.006, 95%CI 1.002~1.009) and α-syn (P = 0.004, OR = 1.001, 95%CI 1.000~1.001) were positively associated with the occurrence rate of POD, while Aβ42 (P < 0.001, OR = 0.989, 95%CI 0.986~0.993), CSF PGRN (P = 0.002, OR = 0.999, 95%CI 0.999~1.000), Aβ42/ t-Tau (P < 0.001, OR = 0.181, 95%CI 0.102~0.319) and Aβ42/p-Tau (P < 0.001, OR = 0.617, 95%CI 0.526~0.725) were inversely proportional to the occurrence of POD. ROC curve analysis indicated that Aβ42/t-Tau (AUC = 0.823), CSF Aβ42 (AUC = 0.813), Aβ42/p-Tau (AUC = 0.810), α-syn (AUC = 0.644) and PGRN (AUC = 0.638) could predict the occurrence rate of POD. The combination of all these biomarkers showed a greater AUC(0.896) than using any of them alone.ConclusionsCSF Aβ42, PGRN, α-syn, Aβ42/t-Tau and Aβ42/p-Tau might be associated with the occurrence rate of POD in patients undergoing knee/hip arthroplasty.Trial registrationClinical Registration No. ChiCTR2000033439.

Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China

Runoff prediction is of great significance to flood defense. However, due to the complexity and randomness of the runoff process, it is hard to predict daily runoff accurately, especially for peak runoff. To address this issue, this study proposes an enhanced long short-term memory (LSTM) model for runoff prediction, where novel loss functions are introduced and feature extractors are integrated. Two loss functions (peak error tanh (PET), peak error swish (PES)) are designed to strengthen the importance of the peak runoff's prediction while weakening the weight of the normal runoff's prediction. The feature extractor consisting of three LSTM networks is established for each meteorological station, aiming to extract temporal features of the input data at each station. Taking the upper Huai River Basin in China as a case study, daily runoff from 1960–2016 is predicted using the enhanced LSTM model. Results indicate that the enhanced LSTM model performed well, achieving Nash–Sutcliffe efficiency (NSE) coefficient ranging from 0.917–0.924 during the validation period (November 2005–December 2016), outperforming the widely used lumped hydrological models (Australian Water Balance Model (AWBM), Sacramento, SimHyd and Tank Model) and the data-driven models (artificial neural network (ANN), support vector regression (SVR), and gated recurrent units (GRU)). The enhanced LSTM with PES as loss function performed best on extreme runoff prediction with a mean NSE for floods of 0.873. In addition, precipitation at a meteorological station with a higher altitude contributes more runoff prediction than the closest stations. This study provides an effective tool for daily runoff prediction, which will benefit the basin's flood defense and water security management.

Hybrid forecasting models for wind-PV systems in diverse geographical locations: Performance and power potential analysis

In order to combat the global warming, much stress is put on integrating non-conventional energy resources, such as wind power plants and solar energy systems, into standard electricity generation systems. The stochastic nature of wind energy at a large scale requires precise forecasting in short and medium terms to manage electricity demands for regional grids because the safety and control of electricity systems are highly sensitive to voltage drop. Unfortunately, the existing forecasting models achieve less desirable performance in short term power forecasting. To resolve this issue, based on hybrid stochastic algorithm based intelligent models, which are proven to be a highly effective predictive tool, this paper proposes two new hybrid models using modified white shark optimization (MWSO) algorithm based GRNN and RBFN. To enhance the forecasting performance, the parameter mv is projected as a function of time to enforce the convergence by accelerating computation as well for effective exploration and exploitation to reinforce features of data. The supervised control and data acquisition (SCADA) data provide the input metrics to the system. A hybrid decomposition model is used to prep data for feature enhancement. Six case studies are formulated from Turkey and Malaysia to validate the forecasting performance of our technically advanced models. Recommended modifications allow MWSO for enhanced training. The results are compared with four hybrid ANN topologies in combinations with PSO and BMO stochastic optimization algorithms. Seasonal results are compared graphically and statistically through 15 min ahead forecasting. The results show that the proposed MWSO-RBFNN model outperforms the classic models in all cases for point forecasting and interval forecasting with higher convergence rate and lower stochastic error. The point and interval forecasting accuracy is extremely important to ensure power systems’ stability, efficiency, and safety. Our model achieves Nash-Sutcliffe constant score of 0.99 and exhibits superior performance with the least RMSE, RE, R2, and MAE.

Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy

Background: Diabetic peripheral neuropathy (DPN) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of DPN is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DPN. Methods: 3012 patients with T2DM were retrospectively studied. These patients were hospitalized between 1 January 2017 and 31 December 2020 in the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China. A total of 901 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China who were hospitalized between 1 January 2019 and 31 December 2020 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were performed to identify independent predictors and establish a nomogram to predict the occurrence of DPN. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and a decision curve analysis (DCA). Findings: Age, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), and fasting blood glucose (FBG) were used to establish a nomogram model for predicting the risk of DPN. In the training and validation cohorts, the areas under the curve of the nomogram constructed from the above six factors were 0.8256 (95% CI: 0.8104-0.8408) and 0.8608 (95% CI: 0.8376-0.8840), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. Interpretation: This study has developed and externally validated a nomogram model which exhibits good predictive ability in assessing DPN risk among the type 2 diabetes population. It provided clinicians with an accurate and effective tool for the early prediction and timely management of DPN.

Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data

Although Deep Learning (DL) models have been introduced in various fields as effective prediction tools, they often do not care about uncertainty. This can be a barrier to their adoption in real-world applications. The current paper aims to apply and evaluate Monte Carlo (MC) dropout, a computationally efficient approach, to investigate the reliability of several skip connection-based Convolutional Neural Network (CNN) models while keeping their high accuracy. To do so, a high-dimensional regression problem is considered in the context of subterranean fluid flow modeling using 376,250 generated samples. The results demonstrate the effectiveness of MC dropout in terms of reliability with a Standard Deviation (SD) of 0.012–0.174, and of accuracy with a coefficient of determination (R2) of 0.7881–0.9584 and Mean Squared Error (MSE) of 0.0113–0.0508, respectively. The findings of this study may contribute to the distribution of pressure in the development of oil/gas fields.

Simulating multiple scenarios of land use/cover change using a coupled model to capture ecological and economic effects

AbstractThe conflict between the demands of ecological protection and economic development is increasingly prominent. Land use change is sensitive to whether economic or ecological profits dominate, and prediction of future land use changes has become an important scientific issue. This article constructs a coupled model that comprises the grey model (GM), the fuzzy multiple objective linear programming model (FMOLP) and the future land use simulation model (FLUS). We used this model to calculate a macro‐scale quantitative forecast using the top‐down GM‐FMOLP model, and calculated micro‐scale spatial simulations of land use/cover change (LUCC) using the bottom‐up FLUS model under three dissimilar future scenarios, with different emphases on ecological and economic benefits. The scenarios were designed to meet different planning requirements in Chongqing: ecological benefits maximization, economic benefits maximization, and combined ecological and economic benefits maximization. The proposed model, FLUS, was applied to a LUCC simulation for Chongqing in 2015, beginning from verified historical land use data from 2010. The results show that the simulation agrees well with the actual land use in 2015. The overall accuracy and kappa coefficient are 85.92% and 0.76, respectively, which indicates the model performs well. The three land use types with the highest producer and user accuracies are forest, cropland and water areas. The GM‐FMOLP model was used to forecast future LUCC quantity demand, and the FLUS model was then used to calculate spatial predictions for the three scenarios for 2040. The prediction results for the six land use types were significantly different under the three different scenarios. The total areas of forest, urban land and water increased by different degrees under the three scenarios, and the unused land area dwindled. Cropland areas were largely converted into forest, urban land and water areas. The increases in forest and urban land areas generally represents internal gap‐filling between disparate areas and peripheral expansion of areas of the same land use type. The increase in water area results from increased surface runoff in mountainous valleys with severe terrain. Areas of unused land are fully transformed into other land use types. The outcomes from the designed scenarios demonstrate that the proposed models are reliable and effective for future LUCC simulation, and highlight key areas where land use changes differently according to the scenario. In summary, in the predictions for the scenario that combined ecological and economic benefits maximization, each land use type tried to maximize ecological and economic benefits under the constraints, taking into account ecological safety and economic growth, which is in line with the coordination, comprehensiveness and binding requirements in Chongqing's development plan. Therefore, this scenario becomes the optimal scenario for land use optimization in Chongqing in the future. This study provides an example of the application of simulation and forecast models in land use management, remediation and urban planning, and addresses the growing requirement for low‐cost and effective tools for prediction of dynamic land use succession patterns.

Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes.

Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25-40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.

CFAGO: cross-fusion of network and attributes based on attention mechanism for protein function prediction.

Protein function annotation is fundamental to understanding biological mechanisms. The abundant genome-scale protein-protein interaction (PPI) networks, together with other protein biological attributes, provide rich information for annotating protein functions. As PPI networks and biological attributes describe protein functions from different perspectives, it is highly challenging to cross-fuse them for protein function prediction. Recently, several methods combine the PPI networks and protein attributes via the graph neural networks (GNNs). However, GNNs may inherit or even magnify the bias caused by noisy edges in PPI networks. Besides, GNNs with stacking of many layers may cause the over-smoothing problem of node representations. We develop a novel protein function prediction method, CFAGO, to integrate single-species PPI networks and protein biological attributes via a multi-head attention mechanism. CFAGO is first pre-trained with an encoder-decoder architecture to capture the universal protein representation of the two sources. It is then fine-tuned to learn more effective protein representations for protein function prediction. Benchmark experiments on human and mouse datasets show CFAGO outperforms state-of-the-art single-species network-based methods at least 7.59%, 6.90%, 11.68% in terms of m-AUPR, M-AUPR and Fmax, respectively, demonstrating cross-fusion by multi-head attention mechanism can greatly improve the protein function prediction. We further evaluate the quality of captured protein representations in terms of Davies Bouldin Score, whose results show cross-fused protein representations by multi-head attention mechanism is at least 2.7% better than that of original and concatenated representations. We believe CFAGO is an effective tool for protein function prediction. The source code of CFAGO and experiments data are available at: http://bliulab.net/CFAGO/. Supplementary data are available at Bioinformatics online.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis.

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms' predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan-Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient's sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876-0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648-0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647-0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

Efficient Data Preprocessing with Ensemble Machine Learning Technique for the Early Detection of Chronic Kidney Disease

It is a serious global health concern that chronic kidney disease (CKD) kills millions of people each year as a result of poor lifestyle choices and inherited factors. Effective prediction tools for prior detection are essential due to the growing number of patients with this disease. By utilizing machine learning (ML) approaches, this study aids specialists in studying precautionary measures for CKD through prior detection. The main objective of this paper is to predict and classify chronic kidney disease using ML approaches on a publicly available dataset. The dataset of CKD has been taken from the publicly available and accessible dataset Irvine ML Repository, which included 400 instances. ML methods (Support Vector Machine (SVM), K-Nearest Neighbors (KNN), random forest (RF), Logistic Regression (LR), and Decision Tree (DT) Classifier) are used as base learners and their performance has been compared with eXtreme Gradient Boosting (XGBoost). All ML algorithms are evaluated against different performance parameters: accuracy, recall, precision, and F1-measure. The results indicated that XGBoost outperformed with 98.00% accuracy as compared to other ML algorithms. For policymakers to forecast patterns of CKD in the population, the model put forth in this paper may be helpful. The model may enable careful monitoring of individuals who are at risk, early CKD detection, better resource allocation, and management that is patient-centered.