Excellent Predictive Performance Research Articles

The Relationship between Cardiomyocyte Action Potentials and Ion Concentrations: Machine Learning Prediction Modeling and Analysis of Spontaneous Spiral Wave Generation Mechanisms

The changes in cardiomyocyte action potentials are related to variations in intra- and extracellular ion concentrations. Abnormal ion concentrations can lead to irregular action potentials, subsequently affecting wave propagation in myocardial tissue and potentially resulting in the formation of spiral waves. Therefore, timely monitoring of ion concentration changes is essential. This study presents a novel machine learning classification model that predicts ion concentration changes based on action potential variation data. We conducted simulations using a single-cell model, generating a dataset of 850 action potential variations corresponding to different ion concentration changes. The model demonstrated excellent predictive performance, achieving an accuracy of 0.988 on the test set. Additionally, the causes of spontaneous spiral wave generation in the heart are insufficiently studied. This study presents a new mechanism whereby changes in extracellular potassium ion concentration leads to the spontaneous generation of spiral waves. By constructing composite myocardial tissue containing both myocardial and fibroblast cells, we observed that variations in extracellular potassium ion concentration can either trigger or inhibit cardiomyocyte excitation. We developed three tissue structures, and by appropriately adjusting the extracellular potassium ion concentration, we observed the spontaneous generation of single spiral waves, symmetrical spiral wave pairs, and asymmetrical double spiral waves.

Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach

The sudden eruption of COVID-19 has inflicted tremendous damage to the worldwide economy, and stock markets have become violently volatile due to its negative impact. Therefore, accurate forecasting of stock price index has been playing an essential role in maintaining national economic security and formulating related policies. In this paper, a novel decomposition-ensemble model is proposed to predict the highly fluctuating stock price index. To begin with, the modified ensemble empirical mode decomposition (MEEMD) method is adopted to decompose the original stock price index into subsequences with different frequencies. Then, the last high-frequency subsequence and other subsequences are predicted through multilayer perceptron (MLP) and long short-term memory (LSTM), respectively. Finally, the prediction outcomes of different model subsequences are reconstructed into the ultimate prediction results by utilizing the integration method. Compared with the contrast models, the MEEMD-LSTM-MLP model proposed in our paper not only demonstrates significant advantages in multi-step forecasting for both emerging and developed markets, but also achieves excellent prediction performance amidst the severe market fluctuations triggered by COVID-19. Furthermore, the application of the MEEMD-LSTM-MLP model is extended to financial time series with different data characteristics and market types, which further proves its high applicability and reliability. Therefore, the conducted hybrid MEEMD-LSTM-MLP model is an effective and stable multi-step forecasting tool to provide valuable intelligent technical support for governments and enterprises in complex economic conditions.

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and metastasis of tumors. Nevertheless, a comprehensive analysis of the diverse types of PCD in lung adenocarcinoma (LUAD) is currently lacking. The study encompassed a total of 1481 genes associated with the regulation of 13 distinct PCD patterns. Ten machine learning algorithms were amalgamated into 101 combinations, from which the optimal algorithm was chosen to formulate an artificial intelligence-derived prognostic signature based on the average C-index across four multicenter cohorts. The established optimal cell death index (CDI) model emerged as an independent risk factor for overall survival, demonstrating robust and consistent performance. Notably, CDI exhibited significantly higher accuracy compared to traditional clinical variables and molecular features. It exhibited superior performance than other published models. By integrating CDI with relevant clinical features, a nomogram with excellent predictive performance was developed. LUAD patients with low CDI score had a higher immune modulators, TIDE scores and immune scores, indicating a better immunotherapy benefit. More importantly, we found that the regulation of antigen presentation is the crucial mechanism of PCD. SCG2 is a key molecule that inhibits the malignant progression of LUAD. CDI holds great potential as a robust and promising tool for enhancing clinical outcomes in patients with LUAD.

Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Machine learning prediction of supercapacitor performance of N-doped biochar from biomass wastes based on N-containing groups, element compositions, and pore structures

N-doped biochar has great potential for development in the field of supercapacitors. In this study, Random Forest and Extreme Gradient Boosting models are used to predict the specific capacitance of N-doped biochar. The prediction is based on several features, including pore structure parameters, element composition, N-containing group of N-doped biochar, and electrochemical testing characteristics. Shapley additive explanations and partial dependency plots are used to explore the impact of the features on specific capacitance. Results show that both the Random Forest and the Extreme Gradient Boosting models exhibited excellent prediction performance, with R2 of 0.95 and 0.96, respectively. N-6 contributes more to the higher specific capacitance among the three N-containing groups. According to the partial dependency plots, when the specific surface area, pore size, and degree of graphitization are around 2200 m2/g, 4 nm, and 1, respectively, the specific capacitance of N-doped biochar is about 303 F/g at 1 A/g. In addition, a procedure for predicting the specific capacitance of N-doped biochar is developed based on the PySimpleGUI library and the Extreme Gradient Boosting model. This study provides a reference for the preparation of high specific capacitance of N-doped biochar.

Sonocatalytic degradation of caffeine using CeO2 nanorods: Modeling by artificial neural network

This study has investigated the utilization of CeO2 nanorods (NRs) in the sonocatalytic degradation of caffeine. The degradation performance was determined by examining the influence of three parametric conditions, namely, the initial pH of the solution (3.5 – 7.5), initial concentration of caffeine (5 – 30 mg/L), and dosage of CeO2 NRs (0.5 – 2.0 g/L). All experiments were conducted in an ultrasonic bath (37 kHz, 150 W) that served as a sonocatalytic reactor. The mathematical modeling of the process with the catalyst was developed using Feedforward artificial neural networks (FFNN). The FFNN was employed to develop suitable modeling for determining the performance of the sonocatalytic degradation of caffeine (%) using CeO2 NRs. A three-layer FFNN with [4−10-1] topology was successfully developed to predict the sonocatalytic degradation of caffeine using CeO2 NRs. The FFNN was able to offer highly accurate predictions with the overall R2 and MSE validation values of 0.991 and 0.00225, respectively. The ANN model has also provided excellent predictive performance by achieving the highest R2 value. Thus, these results showed the promising finding of the sonocatalysis degradation of caffeine using CeO2 NRs via experiments and the ANN model.

Resource potential evaluation of magmatic cobalt and nickel in the east Kunlun metallogenic belt, northwest of China through a geological-constrained convolutional neural network model

In the Kunlun orogenic belt of the East Kunlun region, several magmatic sulfide deposits of cobalt–nickel type, such as Xiarihamu and Shitoukengde, have been discovered along the Kunlun fault. The area still holds significant mineral exploration potential. This paper proposes a convolutional neural network (CNN) model based on geological constraints. When constructing the CNN model, a layer is added to restrict the magnesium-iron ratio content of mafic–ultramafic rocks in the convolutional layer, based on the characteristics of mafic–ultramafic rocks in cobalt–nickel deposits. This layer acts as a hard constraint to filter mafic–ultramafic rocks in the evidence layer, providing theoretical interpretability to the data-driven CNN model. To ensure sample balance in the prediction data and prevent overfitting during the model prediction process, a sliding window method is adopted to expand the positive samples. Predictive models are established before and after sample expansion, and ROC is used to evaluate the predictive models. Based on research into the metallogenic geological background and metallogenic model of the East Kunlun Orogenic Belt, combined with multi-source geological, geophysical, and geochemical data, a geological-constrained CNN model was used to analyze the mineralization potential of magmatic cobalt–nickel deposits in the study area. The research results show that the accuracy of the geology-constrained CNN model is 92.1 %, indicating excellent predictive performance. The results, highly consistent with known deposits, suggest that the model’s predictions can be used for resource potential assessment of magmatic cobalt–nickel deposits in the East Kunlun metallogenic belt in northwest China.

Coagulation Risk Predicting in Anticoagulant-Free Continuous Renal Replacement Therapy.

Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal blood purification therapy to replace impaired renal function. Typically, CRRT therapy requires routine anticoagulation, but for patients at risk of bleeding and with contraindications to sodium citrate, anticoagulant-free dialysis therapy is necessary. However, this approach increases the risk of CRRT circuit coagulation, leading to treatment interruption and increased resource consumption. In this study, we utilized artificial intelligence machine learning methods to predict the risk of CRRT circuit coagulation based on pre-CRRT treatment metrics. We retrospectively analyzed 212 patients who underwent anticoagulant-free CRRT from October 2022 to October 2023. Patients were categorized into high-risk and low-risk groups based on CRRT circuit coagulation within 24 h. We employed eight machine learning methods to predict the risk of circuit coagulation. The performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic. 5-fold cross-validation was used to validate the machine learning models. Feature importance and SHAP plots were used to interpret the model's performance and key drivers. We identified 88 patients (41.51%) at high risk of circuit coagulation within 24 h of CRRT. Our machine learning models showed excellent predictive performance, with ensemble learning achieving an AUC of 0.863 (95% CI: 0.860-0.868), outperforming individual algorithms. Random forest was the best single-algorithm model, with an AUC of 0.819 (95% CI: 0.814-0.823). The top three features identified as most important by the SHAP summary plot and feature importance graph are platelet, filtration fraction (FF), and triglycerides. We created a model using machine learning to predict the risk of circuit coagulation during anticoagulant-free CRRT therapy. Our model performs well (AUC 0.863) and identifies key factors like platelets, FF, and triglycerides. This facilitates the development of personalized treatment strategies by clinicians aimed at reducing circuit coagulation risk, thereby enhancing patient outcomes and reducing healthcare expenses.

Modeling the richness and spatial distribution of the wild relatives of Iranian pears (Pyrus L.) for conservation management

The preservation of the genetic resources of crop wild relatives (CWRs) is crucial for food production systems and is considered a vital measure for global agricultural health and food security. The identification of potential areas where CWRs can thrive is one of the first steps towards their conservation. In this study, we used a maximum entropy model (MaxEnt) to determine the habitat suitability of seven wild relatives of pears (Pyrus L.) for the first time. We aimed to identify high-priority areas for conservation and determine the hotspots for rich biodiversity in Iran. The study showed excellent predictive performance for all species studied (AUC value ≥ 90). The soil depth, solar radiation, minimum temperature of the coldest month (Bio6), and precipitation of the wettest quarter (Bio16) were the main environmental factors that influenced the habitat suitability of all seven species, according to permutation importance. The projected maps revealed that P. elaeagnifolia had the largest suitable habitat area, while P. glabra had the lowest. The results also showed that less than 5% of the suitable habitats for these seven species were in protected areas. This research highlights the need for national preservation policies and the development of cultivation and rehabilitation strategies for these threatened species.

Early Identification of Metabolic Syndrome in Adults of Jiaxing, China: Utilizing a Multifactor Logistic Regression Model

Early Identification of Metabolic Syndrome in Adults of Jiaxing, China: Utilizing a Multifactor Logistic Regression Model

A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions.

In Uganda, the absence of a unified dataset for constructing machine learning models to predict Foot and Mouth Disease outbreaks hinders preparedness. Although machine learning models exhibit excellent predictive performance for Foot and Mouth Disease outbreaks under stationary conditions, they are susceptible to performance degradation in non-stationary environments. Rainfall and temperature are key factors influencing these outbreaks, and their variability due to climate change can significantly impact predictive performance. This study created a unified Foot and Mouth Disease dataset by integrating disparate sources and pre-processing data using mean imputation, duplicate removal, visualization, and merging techniques. To evaluate performance degradation, seven machine learning models were trained and assessed using metrics including accuracy, area under the receiver operating characteristic curve, recall, precision and F1-score. The dataset showed a significant class imbalance with more non-outbreaks than outbreaks, requiring data augmentation methods. Variability in rainfall and temperature impacted predictive performance, causing notable degradation. Random Forest with borderline SMOTE was the top-performing model in a stationary environment, achieving 92% accuracy, 0.97 area under the receiver operating characteristic curve, 0.94 recall, 0.90 precision, and 0.92 F1-score. However, under varying distributions, all models exhibited significant performance degradation, with random forest accuracy dropping to 46%, area under the receiver operating characteristic curve to 0.58, recall to 0.03, precision to 0.24, and F1-score to 0.06. This study underscores the creation of a unified Foot and Mouth Disease dataset for Uganda and reveals significant performance degradation in seven machine learning models under varying distributions. These findings highlight the need for new methods to address the impact of distribution variability on predictive performance.

An ultrasound-based nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma.

Performing prophylactic central lymph node dissection for papillary thyroid microcarcinoma (PTMC) patients with clinically negative lymph node metastasis remains controversial - not all patients with PTMC are suitable for active surveillance. Therefore, we aimed to establish a nomogram based on ultrasound features for predicting CLNM in PTMC. This retrospective study included 636 patients with PTMC, in which the CLNM status was pathologically confirmed. Univariate and multivariate regression analyses were conducted to screen for risk factors associated with CLNM. Then, a CLNM prediction model was established, receiver operating characteristic, calibration, and decision curve analyses were used to assess the model's performance. Five variables, including age, sex, combined CLNM status, tumor size, and capsule invasion, were included in the nomogram. The values of the area under the receiver operating characteristic curve in the training and validation datasets were 0.720 (95% confidence interval [CI], 0.649-0.791) and 0.704 (95% CI, 0.622-0.786), respectively. An ultrasound-based nomogram was successfully established, of which the predictive model shows excellent predictive performance and can be used to evaluate the status of CLNM in PTMC. Thus, patients with high nomogram scores should be considered for prophylactic central neck dissection.

CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation.

Cyclic peptides are versatile therapeutic agents that boast high binding affinity, minimal toxicity, and the potential to engage challenging protein targets. However, the pharmaceutical utility of cyclic peptides is limited by their low membrane permeability-an essential indicator of oral bioavailability and intracellular targeting. Current machine learning-based models of cyclic peptide permeability show variable performance owing to the limitations of experimental data. Furthermore, these methods use features derived from the whole molecule that have traditionally been used to predict small molecules and ignore the unique structural properties of cyclic peptides. This study presents CycPeptMP: an accurate and efficient method to predict cyclic peptide membrane permeability. We designed features for cyclic peptides at the atom-, monomer-, and peptide-levels and seamlessly integrated these into a fusion model using deep learning technology. Additionally, we applied various data augmentation techniques to enhance model training efficiency using the latest data. The fusion model exhibited excellent prediction performance for the logarithm of permeability, with a mean absolute error of $0.355$ and correlation coefficient of $0.883$. Ablation studies demonstrated that all feature levels contributed and were relatively essential to predicting membrane permeability, confirming the effectiveness of augmentation to improve prediction accuracy. A comparison with a molecular dynamics-based method showed that CycPeptMP accurately predicted peptide permeability, which is otherwise difficult to predict using simulations.

Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer.

We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning. We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC. We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model's performance, comparing its results against a single modality of US or DOT. The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model's ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction. The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.

M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy

A wide variety of chemicals cannot be introduced to the marketplace because of their high allergenicity. Therefore, it is fundamentally crucial to assess the allergenic potential of chemicals before introducing them into clinical therapeutics. However, assessing the allergenicity of chemical compounds experimentally is time-consuming and costly. To tackle this challenge, we propose M3S-ALG, a novel multi-step stacking strategy (M3S) for rapid and accurate identification of the allergenicity of chemical compounds by using only the SMILES notation. The proposed M3S method involves three steps, as follows. First, ten different balanced datasets were constructed using an under-sampling approach. Second, for each balanced dataset, 144 base-classifiers were trained and optimized to generate the prediction scores of allergenic chemical compounds considered as new probabilistic features. Third, we selected the important probabilistic features and employed them to construct the final stacked model (M3S-ALG). Experimental results show that M3S-ALG outperforms conventional ensemble strategies and its constituent base-classifiers on both the training and independent test datasets. This indicates the effectiveness and robustness of our proposed strategy in identifying the allergenicity of chemical compounds. In addition, M3S-ALG exhibited excellent prediction performance compared to existing methods on the independent test dataset, achieving a balanced accuracy of 0.877, MCC of 0.712, and AUC of 0.931. Finally, we developed a user-friendly online web server at https://pmlabqsar.pythonanywhere.com/M3SALG. This new approach is anticipated to facilitate the drug discovery and development community for the large-scale identification of chemical compounds with no allergenic properties.

Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation

Secondary structure prediction is a key step in understanding protein function and biological properties and is highly important in the fields of new drug development, disease treatment, bioengineering, etc. Accurately predicting the secondary structure of proteins helps to reveal how proteins are folded and how they function in cells. The application of deep learning models in protein structure prediction is particularly important because of their ability to process complex sequence information and extract meaningful patterns and features, thus significantly improving the accuracy and efficiency of prediction. In this study, a combined model integrating an improved temporal convolutional network (TCN), bidirectional long short-term memory (BiLSTM), and a multi-head attention (MHA) mechanism is proposed to enhance the accuracy of protein prediction in both eight-state and three-state structures. One-hot encoding features and word vector representations of physicochemical properties are incorporated. A significant emphasis is placed on knowledge distillation techniques utilizing the ProtT5 pretrained model, leading to performance improvements. The improved TCN, achieved through multiscale fusion and bidirectional operations, allows for better extraction of amino acid sequence features than traditional TCN models. The model demonstrated excellent prediction performance on multiple datasets. For the TS115, CB513 and PDB (2018–2020) datasets, the prediction accuracy of the eight-state structure of the six datasets in this paper reached 88.2%, 84.9%, and 95.3%, respectively, and the prediction accuracy of the three-state structure reached 91.3%, 90.3%, and 96.8%, respectively. This study not only improves the accuracy of protein secondary structure prediction but also provides an important tool for understanding protein structure and function, which is particularly applicable to resource-constrained contexts and provides a valuable tool for understanding protein structure and function.

Borehole Breakout Prediction Based on Multi-Output Machine Learning Models Using the Walrus Optimization Algorithm

Borehole breakouts significantly influence drilling operations’ efficiency and economics. Accurate evaluation of breakout size (angle and depth) can enhance drilling strategies and hold potential for in situ stress magnitude inversion. In this study, borehole breakout size is approached as a complex nonlinear problem with multiple inputs and outputs. Three hybrid multi-output models, integrating commonly used machine learning algorithms (artificial neural networks ANN, random forests RF, and Boost) with the Walrus optimization algorithm (WAOA) optimization techniques, are developed. Input features are determined through literature research (friction angle, cohesion, rock modulus, Poisson’s ratio, mud pressure, borehole radius, in situ stress), and 501 related datasets are collected to construct the borehole breakout size dataset. Model performance is assessed using the Pearson Correlation Coefficient (R2), Mean Absolute Error (MAE), Variance Accounted For (VAF), and Root Mean Squared Error (RMSE). Results indicate that WAOA-ANN exhibits excellent and stable prediction performance, particularly on the test set, outperforming the single-output ANN model. Additionally, SHAP sensitivity analysis conducted on the WAOA-ANN model reveals that maximum horizontal principal stress (σH) is the most influential parameter in predicting both the angle and depth of borehole breakout. Combining the results of the studies and analyses conducted, WAOA-ANN is considered to be an effective hybrid multi-output model in the prediction of borehole breakout size.

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning

Background and aimsThe recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatures to predict the recurrence in PTC.MethodsA total of 554 patients with PTC who underwent ultrasound imaging before total thyroidectomy were included. Among them, 79 experienced at least one recurrence. Then 388 were divided into the training cohort and 166 into the validation cohort. The radiomics features were extracted from the region of interest (ROI) we manually drew on the tumor image. The feature selection was conducted using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. And multivariate Cox regression analysis was used to build the combined nomogram using radiomics signatures and significant clinicopathological characteristics. The efficiency of the nomogram was evaluated by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to analyze the recurrence-free survival (RFS) in different radiomics scores (Rad-scores) and risk scores.ResultsThe combined nomogram demonstrated the best performance and achieved an area under the curve (AUC) of 0.851 (95% CI: 0.788 to 0.913) in comparison to that of the radiomics signature and the clinical model in the training cohort at 3 years. In the validation cohort, the combined nomogram (AUC = 0.885, 95% CI: 0.805 to 0.930) also performed better. The calibration curves and DCA verified the clinical usefulness of combined nomogram. And the Kaplan-Meier analysis showed that in the training cohort, the cumulative RFS in patients with higher Rad-score was significantly lower than that in patients with lower Rad-score (92.0% vs. 71.9%, log rank P < 0.001), and the cumulative RFS in patients with higher risk score was significantly lower than that in patients with lower risk score (97.5% vs. 73.5%, log rank P < 0.001). In the validation cohort, patients with a higher Rad-score and a higher risk score also had a significantly lower RFS.ConclusionWe proposed a nomogram combining clinicopathological variables and ultrasound radiomics signatures with excellent performance for recurrence prediction in PTC patients.

3MTox: A motif-level graph-based multi-view chemical language model for toxicity identification with deep interpretation

Toxicity identification plays a key role in maintaining human health, as it can alert humans to the potential hazards caused by long-term exposure to a wide variety of chemical compounds. Experimental methods for determining toxicity are time-consuming, and costly, while computational methods offer an alternative for the early identification of toxicity. For example, some classical ML and DL methods, which demonstrate excellent performance in toxicity prediction. However, these methods also have some defects, such as over-reliance on artificial features and easy overfitting, etc. Proposing novel models with superior prediction performance is still an urgent task. In this study, we propose a motifs-level graph-based multi-view pretraining language model, called 3MTox, for toxicity identification. The 3MTox model uses Bidirectional Encoder Representations from Transformers (BERT) as the backbone framework, and a motif graph as input. The results of extensive experiments showed that our 3MTox model achieved state-of-the-art performance on toxicity benchmark datasets and outperformed the baseline models considered. In addition, the interpretability of the model ensures that the it can quickly and accurately identify toxicity sites in a given molecule, thereby contributing to the determination of the status of toxicity and associated analyses. We think that the 3MTox model is among the most promising tools that are currently available for toxicity identification.

Prognostic significance of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in breast cancer: a propensity score-matching study

BackgroundThe hemoglobin-albumin-lymphocyte-platelet (HALP) score functions as a comprehensive index that assesses the systemic inflammatory response, nutritional, and immune status. This study aimed to explore the relationship between preoperative HALP score and the prognosis of BC patients and to develop predictive nomograms.MethodsClinicopathological data were collected for BC patients who underwent mastectomy between December 2010 and April 2014 from Sun Yat-sen University Cancer Center. The optimal cutoff value for HALP was determined by maximally selected rank statistics for overall survival data. Propensity score matching (PSM) was applied to develop comparable cohorts of high-HALP group and low-HALP group. Kaplan–Meier curves and Cox regression analyses were performed to determine the impact of HALP on BC patients. Prognostic nomograms were developed based on the multivariate Cox regression method. Then, the concordance index (C-index), calibration plots, and decision curves analysis (DCA) were applied to evaluate the prognostic performance of the nomograms.ResultsA total of 1,856 patients were included as the primary cohort, and 1,470 patients were matched and considered as the PSM cohort. In the primary cohort, the 5-year overall survival (OS) and progression-free survival (PFS) rates for high-HALP group (≥ 47.89) and low-HALP group (< 47.89) were 94.4% vs. 91.0% (P = 0.005) and 87.8% vs. 82.1% (P = 0.005), respectively. Similar results were observed in PSM cohort (5-year OS, 94.3% vs. 90.8%, P = 0.015; 5-year PFS, 87.5% vs. 83.2%, P = 0.036). Notably, multivariate Cox regression analysis in the PSM cohort showed that HALP could independently predict BC patient prognosis in both OS (HR: 0.596, 95%CI [0.405–0.875], P = 0.008) and PFS (HR: 0.707, 95%CI [0.538–0.930], P = 0.013). OS and PFS nomograms showed excellent predictive performance with the C-indexes of 0.783 and 0.720, respectively. The calibration plots and DCA also indicated the good predictability of the nomograms. Finally, subgroup analysis further demonstrated a favorable impact of HALP on both OS and PFS.ConclusionPreoperative HALP score can be used as a reliable independent predictor of OS and PFS in BC patients, and the nomograms may provide a personalized treatment strategy.