Graph neural networks (GNNs) have shown great promise in modeling graph-structured data, but the over-smoothing problem restricts their effectiveness in deep layers. Two key weaknesses of existing research on deep GNN models are: (1) ignoring the beneficial aspects of intra-class smoothing while focusing solely on reducing inter-class smoothing, and (2) inefficient computation of residual weights that neglect the influence of neighboring nodes' distributions. To address these weaknesses, we propose a novel Smoothing Deceleration (SD) strategy to reduce the smoothing speed rate of nodes as information propagates between layers, thereby mitigating over-smoothing. Firstly, we analyze the smoothing speed rate of node representations between layers by differential operations. Subsequently, based on this analysis, we introduce two innovative modules: Class-Related Smoothing Deceleration (CR-SD) loss and Smooth Deceleration Residual (NAR). CR-SD loss first takes into account the duality of smoothing, reducing inter-class smoothing while preserving the benefits of intra-class smoothing, thus reducing over-smoothing while maintaining model performance. NAR is specifically designed for graph-structured data, integrating the distribution of neighboring nodes, and is a novel method for computing residual weights. Finally, the comparative experimental results demonstrate that our SD strategy can extend existing shallow GNNs to deeper and delivers superior performance compared to both vanilla models and existing deep GNNs. And, a series of analytical experiments be conducted to prove that our proposed SD strategy effectively mitigates over-smoothing in deep GNNs. The source code for this work is available at https://github.com/cheng-qi/sd.

Introduction:CAR-T cell therapy is a novel approach in the treatment of hematological tumors. However, it is associated with life-threatening side effects, such as the severe cytokine release syndrome (sCRS). Therefore, predicting the occurrence and development of sCRS is of great significance for clinical CAR-T therapy. The study of existing clinical data by artificial intelligence may bring useful information.Methods:By analyzing the heat map of clinical factors and comparing them between severe and non-severe CRS, we can identify significant differences among these factors and understand their interrelationships. Ultimately, a decision tree approach was employed to predict the timing of severe CRS in both children and adults, considering variables such as the same day, the day before, and initial values.Results:We measured cytokines and clinical biomarkers in 202 patients who received CAR-T therapy. Peak levels of 25 clinical factors, including IFN-γ, IL6, IL10, ferritin, and D-dimer, were highly associated with severe CRS after CAR T cell infusion. Using the decision tree model, we were able to accurately predict which patients would develop severe CRS consisting of three clinical factors, classified as same-day, day-ahead, and initial value prediction. Changes in serum biomarkers, including C-reactive protein and ferritin, were associated with CRS, but did not alone predict the development of severe CRS.Conclusion:Our research will provide significant information for the timely prevention and treatment of sCRS, during CAR-T immunotherapy for tumors, which is essential to reduce the mortality rate of patients.

Silencing mRNA through siRNA is vital for RNA interference (RNAi), necessitating accurate computational methods for siRNA selection. Current approaches, relying on machine learning, often face challenges with large data requirements and intricate data preprocessing, leading to reduced accuracy. To address this challenge, we propose a BERT model-based siRNA target gene knockdown efficiency prediction method called BERT-siRNA, which consists of a pre-trained DNA-BERT module and Multilayer Perceptron module. It applies the concept of transfer learning to avoid the limitation of a small sample size and the need for extensive preprocessing processes. By fine-tuning on various siRNA datasets after pretraining on extensive genomic data using DNA-BERT to enhance predictive capabilities. Our model clearly outperforms all existing siRNA prediction models through testing on the independent public siRNA dataset. Furthermore, the model's consistent predictions of highefficiency siRNA knockdown for SARS-CoV-2, as well as its alignment with experimental results for PDCD1, CD38, and IL6, demonstrate the reliability and stability of the model. In addition, the attention scores for all 19nt positions in the dataset indicate that the model's attention is predominantly focused on the 5 ' end of the siRNA. The step-by-step visualization of the hidden layer's classification progressively clarified and explained the effective feature extraction of the MLP layer. The explainability of model by analysis the attention scores and hidden layers is also our main purpose in this work, making it more explainable and reliable for biological researchers.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

BACKGROUND:CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately.RESULTS:We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions.CONCLUSIONS:Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .

Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models on NER tasks. Nonetheless, the effectiveness of these NER techniques relies on the availability of substantial volumes of annotated data, which is labor-intensive and inefficient. Methods: This study introduces a novel approach that diverges from the conventional reliance on manually annotated data. It employs a transformer-based technique known as Positive-Unlabeled Learning (PULearning), which incorporates adaptive learning and is applied to the clinical cancer drug toxicity corpus. To improve the precision of prediction, we employ relative position embeddings within the transformer encoder. Additionally, we formulate a composite loss function that integrates two Kullback-Leibler (KL) regularizers to align with PULearning assumptions. The outcomes demonstrate that our approach attains the targeted performance for NER tasks, solely relying on unlabeled data and named entity dictionaries. Conclusion: Our model achieves an overall NER performance with an F1 of 0.819. Specifically, it attains F1 of 0.841, 0.801 and 0.815 for DRUG, CANCER, and TOXI entities, respectively. A comprehensive analysis of the results validates the effectiveness of our approach in comparison to existing PULearning methods on biomedical NER tasks. Additionally, a visualization of the associations among three identified entities is provided, offering a valuable reference for querying their interrelationships.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

Background: Chimeric Antigen Receptor (CAR)-T cell therapy has emerged as a highly effective treatment for hematological tumors. However, the associated adverse reaction, Cytokine Release Syndrome (CRS), poses a significant challenge. While numerous studies have investigated CRS biomarkers during CAR-T cell therapy, the ability to predict CRS risk prior to treatment initiation remains a crucial yet underexplored aspect. Objective: The primary purpose of this study was to address the issue of limited data, explore an alternative approach using public data to identify predictive markers for CRS risk assessment from RNA-Seq in pre-treatment patients data, and comprehend the inducible mechanisms underlying CRS. Methods: We integrated information from two public databases, the FDA Adverse Event Reporting System (FAERS) for adverse reaction reports of CAR-T cell therapy and the Cancer Genome Atlas (TCGA) for RNA-Seq data on corresponding hematological tumors. Candidate genes were screened by correlation analysis between Reported Odds Ratio (ROR) values and RNA-Seq gene expression levels, and then core factors were identified through stepwise analysis of pathway enrichment, cluster analysis, and protein interactions. Results: Our analysis highlighted the correlation between CRS risk and pre-treatment T cell activation/ proliferation, identifying key genes (IFN-γ, IL1β, IL2, IL6, and IL10) as significant CRS indicators. Conclusion: This study offers a unique perspective on predicting CRS risk before CAR-T cell therapy, circumventing the challenges of scarce clinical data by leveraging analysis of public databases. It elucidates the crucial role of T cell activation/proliferation dynamics in CRS. The analytical methods and identified markers provide a reference for the research and clinical application of CAR-T cell therapy.

Background With the COVID-19 outbreak, an increasing number of individuals are concerned about their health, particularly their immune status. However, as of now, there is no available algorithm that effectively assesses the immune status of normal, healthy individuals. In response to this, a new score-based method is proposed that utilizes complete blood cell counts (CBC) to provide early warning of disease risks, such as COVID-19. Methods First, data on immune-related CBC measurements from 16,715 healthy individuals were collected. Then, a three-platform model was developed to normalize the data, and a Gaussian mixture model was optimized with expectation maximization (EM-GMM) to cluster the immune status of healthy individuals. Based on the results, Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) were used to determine the correlation of each CBC index with the immune status. Consequently, a weighted sum model was constructed to calculate a continuous immunity score, enabling the evaluation of immune status. Results The results demonstrated a significant negative correlation between the immunity score and the age of healthy individuals, thereby validating the effectiveness of the proposed method. In addition, a nonlinear polynomial regression model was developed to depict this trend. By comparing an individual’s immune status with the reference value corresponding to their age, their immune status can be evaluated. Conclusion In summary, this study has established a novel model for evaluating the immune status of healthy individuals, providing a good approach for early detection of abnormal immune status in healthy individuals. It is helpful in early warning of the risk of infectious diseases and of significant importance.

Drug-drug interaction (DDIs) extraction has become a vital task for biomedical and clinical research and public health safety. There are many excellent and effective methods based on neural network models have been proposed on DDIs extraction task and achieved good performance. However, most of the previous models suffer from the serious problem of imbalance data, which can affect the relation extraction results. In this study, a bidirectional encoder representation from transformer pretrained on biomedical corpora (BioBERT) is applied to DDIs extraction from biomedical literature. After the semantic vectors are obtained to represent the entities in a sentence by BioBERT, the embeddings of two entities in a sentence are averaged into two vectors, concatenate the first token, and go through the fully connected layer with an activation operation. Then, we explore an improved focal loss function to dealing with the imbalance data. The performance of our proposed approach gets an F-score of 80.45% on the DDIExtraction 2013 and achieves a significant improvement over the methods of BioBERT with traditional cross-entropy loss function.

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.

The tumor burden (TB) is significantly related to the severity of cytokine release syndrome (CRS) caused by CAR-T cells, but its correlation with therapeutic efficacy has not been systematically studied. This study focused on the effects of the TB level on both the safety and efficacy of ssCART-19 as a treatment for r/r B-ALL. Taking the 5% tumor burden as the boundary, the study participants were divided into 2 groups, high and low tumor burden groups. Under this grouping strategy, the impacts of differential r/r B-ALL TBs on the clinical therapeutic efficacy (CR rate and long-term survival) and safety profiles after ssCART-19 cell treatment were analysed. 78 patients were reported in this study. The differential B-ALL TBs significantly affected the complete remission (CR) rates of patients treated with ssCART-19, with rates of 93.94% and 75.56% in the low and high TB groups, respectively (P = 0.0358). The effects of TBs on long-term therapeutic efficacy were further studied based on event-free survival (EFS) and overall survival (OS) profiles; both the OS and EFS of the low TB group were better than those of the high TB group, but the differences were not statistically significant. Importantly, the time points of TB measurement did not significantly affect the OS and EFS profiles regardless of whether the TBs were measured before or after fludarabine-cyclophosphamide (FC) preconditional chemotherapy. On the other hand, the severity of CRS was significantly correlated with the TB level (P = 0.0080), and the incidence of sCRS was significantly related to the TB level (the sCRS incidence increased as the TB level increased, P = 0.0224). Unexpectedly, the ssCART-19 cell expansion peaks were not significantly different (P = 0.2951) between the study groups. Patients with a low r/r B-ALL TB yield more net benefits from CAR-T treatment than those with a high TB in terms of safety and CR rate. These findings are critical and valuable for determining the optimal CAR-T cell treatment window for r/r B-ALL patients and will further the development of comprehensive and reasonable CAR-T cell treatment plans for r/r B-ALL patients with differential TBs. Trial registration: ClinicalTrials.gov identifier, NCT03919240.

BACKGROUND:Chimeric antigen receptor T-cell (CAR-T) therapy is a new and efficient cellular immunotherapy. The therapy shows significant efficacy, but also has serious side effects, collectively known as cytokine release syndrome (CRS). At present, some CRS-related cytokines and their roles in CAR-T therapy have been confirmed by experimental studies. However, the mechanism of CRS remains to be fully understood.METHODS:Based on big data for human protein interactions and meta-learning graph neural network, we employed known CRS-related cytokines to comprehensively investigate the CRS associated cytokines in CAR-T therapy through protein interactions. Subsequently, the clinical data for 119 patients who received CAR-T therapy were examined to validate our prediction results. Finally, we systematically explored the roles of the predicted cytokines in CRS occurrence by protein interaction network analysis, functional enrichment analysis, and pathway crosstalk analysis.RESULTS:We identified some novel cytokines that would play important roles in biological process of CRS, and investigated the biological mechanism of CRS from the perspective of functional analysis.CONCLUSIONS:128 cytokines and related molecules had been found to be closely related to CRS in CAR-T therapy, where several important ones such as IL6, IFN-γ, TNF-α, ICAM-1, VCAM-1 and VEGFA were highlighted, which can be the key factors to predict CRS.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.