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Abstract Background Drug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges. Results Here, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug molecules to pinpoint key local structures essential for DDI discovery. Moreover, DrugDAGT further implements graph contrastive learning to maximize the similarity of representations across different views for better discrimination of molecular structures. Experiments in both warm-start and cold-start scenarios demonstrate that DrugDAGT outperforms state-of-the-art baseline models, achieving superior overall performance. Furthermore, visualization of the learned representations of drug pairs and the attention map provides interpretable insights instead of black-box results. Conclusions DrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT .

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

IntroductionSi-Ni-San (SNS), a traditional Chinese medicine, is effective in treating liver fibrosis with an unclear mechanism. Although disturbance of intestinal flora and the subsequent secretion of short-chain fatty acids (SCFAs) is suggested to be involved in the progression of liver fibrosis, whether SNS produces the anti-fibrosis effect through the regulation of intestinal flora and SCFAs remains unclear.MethodsIn the current study, carbon tetrachloride (CCl4)-treated mice were dosed with SNS to examine the anti-fibrotic effects and the involved mechanism. Biochemical parameters, histological staining, and analyses of fibrotic gene expression were used to evaluate the anti-fibrotic effect of SNS, while intestinal flora and SCFA content were determined by 16S rRNA and LC–MS to evaluate the mechanism.ResultsIn vivo results showed that SNS improved liver function, reduced hepatocyte apoptosis and FFAR2/3 expression, and restored intestinal dysbiosis and reduced PA, BA, and IsA levels. In vitro experiments showed that PA, BA, and IsA exacerbated TNF-α-induced HepG2 apoptosis. Notably, the protective effects of SNS were compromised in pseudo-sterile mice.DiscussionIn conclusion, our experimental results suggest that the disturbance in intestinal flora results in elevated SCFA levels, which further exacerbates hepatocyte apoptosis in liver fibrosis, while SNS suppresses CCl4-induced liver fibrosis at least partially by reinstating intestinal flora homeostasis and reducing SCFA levels.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data.

De novo drug design is crucial in advancing drug discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model's generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery.

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.

It is widely recognized that inequalities in social status cause inequalities in health. Women in a family often directly influence three generations–women themselves, their children and their parents -yet the effect of women’s family status on their own health status and that of the two generations before and after is not clear. Taking data from the China Family Panel Studies, this study used an ordered response model to investigate the effect of childbearing-age women’s family status on the health status of three generations. The results showed that increases in childbearing-age women’s family status improved the health status of the women themselves and their children. Unlike previous studies, however, we found that higher family status did not improve parents’ health status but decreased it. The mechanism analysis indicated that women’s family status influenced the health status of three generations through economic conditions, resource allocation, and child discipline. The results held after robustness testing. Our findings contribute to knowledge in related fields and provide theoretical support for policies that empower women.

Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.

Introduction: The physical interactions between enhancers and promoters are often involved in gene transcriptional regulation. High tissue-specific enhancer-promoter interactions (EPIs) are responsible for the differential expression of genes. Experimental methods are time-consuming and labor-intensive in measuring EPIs. An alternative approach, machine learning, has been widely used to predict EPIs. However, most existing machine learning methods require a large number of functional genomic and epigenomic features as input, which limits the application to different cell lines.Methods: In this paper, we developed a random forest model, HARD (H3K27ac, ATAC-seq, RAD21, and Distance), to predict EPI using only four types of features.Results: Independent tests on a benchmark dataset showed that HARD outperforms other models with the fewest features.Discussion: Our results revealed that chromatin accessibility and the binding of cohesin are important for cell-line-specific EPIs. Furthermore, we trained the HARD model in the GM12878 cell line and performed testing in the HeLa cell line. The cross-cell-lines prediction also performs well, suggesting it has the potential to be applied to other cell lines.

Introduction: Various activities in biological cells are affected by three-dimensional genome structure. The insulators play an important role in the organization of higher-order structure. CTCF is a representative of mammalian insulators, which can produce barriers to prevent the continuous extrusion of chromatin loop. As a multifunctional protein, CTCF has tens of thousands of binding sites in the genome, but only a portion of them can be used as anchors of chromatin loops. It is still unclear how cells select the anchor in the process of chromatin looping.Methods: In this paper, a comparative analysis is performed to investigate the sequence preference and binding strength of anchor and non-anchor CTCF binding sites. Furthermore, a machine learning model based on the CTCF binding intensity and DNA sequence is proposed to predict which CTCF sites can form chromatin loop anchors.Results: The accuracy of the machine learning model that we constructed for predicting the anchor of the chromatin loop mediated by CTCF reached 0.8646. And we find that the formation of loop anchor is mainly influenced by the CTCF binding strength and binding pattern (which can be interpreted as the binding of different zinc fingers).Discussion: In conclusion, our results suggest that The CTCF core motif and it’s flanking sequence may be responsible for the binding specificity. This work contributes to understanding the mechanism of loop anchor selection and provides a reference for the prediction of CTCF-mediated chromatin loops.

The study of DNA binding proteins (DBPs) is of great importance in the biomedical field and plays a key role in this field. At present, many researchers are working on the prediction and detection of DBPs. Traditional DBP prediction mainly uses machine learning methods. Although these methods can obtain relatively high pre-diction accuracy, they consume large quantities of human effort and material resources. Transfer learning has certain advantages in dealing with such prediction problems. Therefore, in the present study, two features were extracted from a protein sequence, a transfer learning method was used, and two classical transfer learning algorithms were compared to transfer samples and construct data sets. In the final step, DBPs are detected by building a deep learning neural network model in a way that uses attention mechanisms.

Abstract Background RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure. In addition, there is a large difference between the number of paired bases and the number of unpaired bases in the RNA sequences, which means the problem of positive and negative sample imbalance is easy to make the model fall into a local optimum. To solve the above problems, this paper proposes a variable-length dynamic bidirectional Gated Recurrent Unit(VLDB GRU) model. The model can accept sequences with different lengths through the introduction of flag vector. The model can also make full use of the base information before and after the predicted base and can avoid losing part of the information due to truncation. Introducing a weight vector to predict the RNA training set by dynamically adjusting each base loss function solves the problem of balanced sample imbalance. Results The algorithm proposed in this paper is compared with the existing algorithms on five representative subsets of the data set RNA STRAND. The experimental results show that the accuracy and Matthews correlation coefficient of the method are improved by 4.7% and 11.4%, respectively. Conclusions The flag vector introduced allows the model to effectively use the information before and after the protein sequence; the introduced weight vector solves the problem of unbalanced sample balance. Compared with other algorithms, the LVDB GRU algorithm proposed in this paper has the best detection results.

There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an accurate method. Through extensive experiments, we have developed a model based on graph-regularized k-local hyperplane distance nearest neighbor model (GHKNN) binary classification. In this, the model uses the physicochemical property extraction method to extract protein sequence features and the SMOTE method to upsample protein sequence features. The combination achieves the most accurate performance for identifying all protein sequences. Finally, we compare the model based on GHKNN binary classification with other classifiers and measure them using four different metrics: SN, SP, ACC, and MCC. In experiments, the model performs significantly better than other classifiers.

High-fat diet (HFD)-induced insulin resistance (IR) in skeletal muscle is often accompanied by mitochondrial dysfunction and oxidative stress. Boosting nicotinamide adenine dinucleotide (NAD) using nicotinamide riboside (NR) can effectively decrease oxidative stress and increase mitochondrial function. However, whether NR can ameliorate IR in skeletal muscle is still inconclusive. We fed male C57BL/6J mice with an HFD (60% fat) ± 400 mg/kg·bw NR for 24 weeks. C2C12 myotube cells were treated with 0.25 mM palmitic acid (PA) ± 0.5 mM NR for 24 h. Indicators for IR and mitochondrial dysfunction were analyzed. NR treatment alleviated IR in HFD-fed mice with regard to improved glucose tolerance and a remarkable decrease in the levels of fasting blood glucose, fasting insulin and HOMA-IR index. NR-treated HFD-fed mice also showed improved metabolic status regarding a significant reduction in body weight and lipid contents in serum and the liver. NR activated AMPK in the skeletal muscle of HFD-fed mice and PA-treated C2C12 myotube cells and upregulated the expression of mitochondria-related transcriptional factors and coactivators, thereby improving mitochondrial function and alleviating oxidative stress. Upon inhibiting AMPK using Compound C, NR lost its ability in enhancing mitochondrial function and protection against IR induced by PA. In summary, improving mitochondrial function through the activation of AMPK pathway in skeletal muscle may play an important role in the amelioration of IR using NR.

Abstract Background Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. Results In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. Conclusion Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.

The prediction of protein function is a common topic in the field of bioinformatics. In recent years, advances in machine learning have inspired a growing number of algorithms for predicting protein function. A large number of parameters and fairly complex neural networks are often used to improve the prediction performance, an approach that is time-consuming and costly. In this study, we leveraged traditional features and machine learning classifiers to boost the performance of vesicle transport protein identification and make the prediction process faster. We adopt the pseudo position-specific scoring matrix (PsePSSM) feature and our proposed new classifier hypergraph regularized k-local hyperplane distance nearest neighbour (HG-HKNN) to classify vesicular transport proteins. We address dataset imbalances with random undersampling. The results show that our strategy has an area under the receiver operating characteristic curve (AUC) of 0.870 and a Matthews correlation coefficient (MCC) of 0.53 on the benchmark dataset, outperforming all state-of-the-art methods on the same dataset, and other metrics of our model are also comparable to existing methods.

Abstract Background DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. Results In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ( $$84.19\%$$ 84.19 % ) and PDB186 ( $$83.7\%$$ 83.7 % ), respectively. Conclusion Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.

Targeting Nicotinamide adenine dinucleotide (NAD) metabolism has emerged as a promising anti-cancer strategy; we aimed to explore the health benefits of boosting NAD levels with nicotinamide riboside (NR) on hepatocellular carcinoma (HCC). We established three in vivo tumor models, including subcutaneous transplantation tumor model in both Balb/c nude mice (xenograft), C57BL/6J mice (allograft), and hematogenous metastatic neoplasm in nude mice. NR (400 mg/kg bw) was supplied daily in gavage. In-situ tumor growth or noninvasive bioluminescence were measured to evaluate the effect of NR on the HCC process. HepG2 cells were treated with transforming growth factor-β (TGF-β) in the absence/presence of NR in vitro. We found that NR supplementation alleviated malignancy-induced weight loss and metastasis to lung in nude mice in both subcutaneous xenograft and hematogenous metastasis models. NR supplementation decreased metastasis to the bone and liver in the hematogenous metastasis model. NR supplementation also significantly decreased the size of allografted tumors and extended the survival time in C57BL/6J mice. In vitro experiments showed that NR intervention inhibited the migration and invasion of HepG2 cells triggered by TGF-β. In summary, our results supply evidence that boosting NAD levels by supplementing NR alleviates HCC progression and metastasis, which may serve as an effective treatment for the suppression of HCC progression.

Abstract Background Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem. Results In this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to study Homo sapiens, we further establish human RNA subcellular localization datasets. Furthermore, we utilize different nucleotide property composition models to extract effective features to adequately represent the important information of nucleotide sequences. In the most critical part, we achieve a major challenge that is to fuse the multivariate information through multiple kernel learning based on Hilbert-Schmidt independence criterion. The optimal combined kernel can be put into an integration support vector machine model for identifying multi-label RNA subcellular localizations. Our method obtained excellent results of 0.703, 0.757, 0.787, and 0.800, respectively on four RNA data sets on average precision. Conclusion To be specific, our novel method performs outstanding rather than other prediction tools on novel benchmark datasets. Moreover, we establish user-friendly web server with the implementation of our method.

Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.

Abstract Background In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult. Results To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal. Conclusions In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.

Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.

Background: The epithelial tight junction is an important intestinal barrier whose disruption can lead to the release of harmful intestinal substances into the circulation and cause damage to systemic injury. The maintenance of intestinal epithelial tight junctions is closely related to energy homeostasis and mitochondrial function. Nicotinamide riboside (NR) is a NAD booster that can enhance mitochondrial biogenesis in liver. However, whether NR can prevent ethanol-induced intestinal barrier dysfunction and the underlying mechanisms remain unclear. Methods: We applied the mouse NIAAA model (chronic plus binge ethanol feeding) and Caco-2 cells to explore the effects of NR on ethanol-induced intestinal barrier dysfunction and the underlying mechanisms. NAD homeostasis and mitochondrial function were measured. In addition, knockdown of SirT1 in Caco-2 cells was further applied to explore the role of SirT1 in the protection of NR. Results: We found that ethanol increased intestinal permeability, increased the release of LPS into the circulation and destroyed the intestinal epithelial barrier structure in mice. NR supplementation attenuated intestinal barrier injury. Both in vivo and in vitro experiments showed that NR attenuated ethanol-induced decreased intestinal tight junction protein expressions and maintained NAD homeostasis. In addition, NR supplementation activated SirT1 activity and increased deacetylation of PGC-1α, and reversed ethanol-induced mitochondrial dysfunction and mitochondrial biogenesis. These effects were diminished with the knockdown of SirT1 in Caco-2 cells. Conclusion: Boosting NAD by NR alleviates ethanol-induced intestinal epithelial barrier damage via protecting mitochondrial function in a SirT1-dependent manner.

Detecting and treating cerebrovascular diseases are essential for the survival of patients with chronic kidney disease (CKD). Machine learning algorithms can be used to effectively predict stroke risk in patients with end-stage renal disease (ESRD). An imbalance in the amount of collected data associated with different risk levels can influence the classification task. Therefore, we propose the use of a kernelized k-local hyperplane nearest-neighbor model (KHKNN) for the effective prediction of stroke risk in patients with ESRD. We compared our proposed method with other conventional machine learning methods, which revealed that our method could effectively perform the task of classifying stroke risk.

Economic development and infrastructure improvement will inevitably lead to the accumulation of construction waste and tailings, which has not only a huge impact on the environment but is also a waste of resources. Recycling these resources and making green concrete is an effective way to solve these problems. In this study, the salt spray erosion characteristics and erosion mechanism of tailings recycled concrete (TRC) with polypropylene fibers were studied through macro and micro methods. The results showed that its compressive strength and splitting tensile strength increased at first and then decreased, with the optimum content of 0.6–0.9%, and the strength increase coefficient reached its maximum value at the erosion period being 14 d to 28 d. Under the same erosion cycle, when the fiber content was low (≤0.6–0.9%), the erosion depth hardly fluctuated. While the fiber content changed from 0.6% to 1.2%, the erosion depth and curing ability (erosion for 90 days) increased by 16.29% and 11.20%, which implied that its erosion resistance decreased sharply. Through SEM microscopic analysis, it could be observed that when the fiber content was low, the matrix structure and porosity had little change; while the fiber content was excessive, the porosity increased greatly. The longer the erosion period was, the greater the cumulative expansion of salt crystals was, and the larger the porosity was, whose results were in good agreement with the experimental results. This research provides a significant theoretical basis for the application of TRC in engineering.

Long non-coding RNA, also known as lncRNA, is a series of single-stranded polynucleotides (no less than 200 nucleotides each), consisting of non-protein coding transcripts. LncRNA plays a crucial role in regulating gene expression, during the transcriptional, post-transcriptional, and epigenetic processes. This is achieved by lncRNA interacts with the corresponding RNA-binding proteins. It has been drawn to a lot of attention that the reduction of the excessive laboratory cost and the increase in speed and accuracy gains benefits from the employment of computational intelligence in lncRNA-protein interaction (LPI) identification. Although numerous pertinent in silico studies of LPI prediction have been proposed, there is still room for enhancing the accuracy of the existing LPI prediction methods. In this paper, we have proposed a novel method for identifying LPI with kernel target alignment based on semi-supervised link prediction (LPI-KTASLP), which adopts multivariate information to predict lncRNAs-proteins interactions. To integrate the heterogeneous kernels, kernel target alignment has been applied to deal with kernel fusion. We have calculated the low-rank approximation matrices of lncRNA and protein, where eigendecomposition is used to reduce computing pressure. The prediction model has been obtained by producing the ultimate LPI prediction matrix. Experimental results show that the prediction ability of the LPI-KTASLP algorithm has surpassed many other LPI prediction schemes. Our method of lncRNA-protein interaction prediction has been evaluated on a standard benchmark dataset of LPIs. We have observed that the highest AUPR of 0.6148 is obtained by our proposed model (LPI-KTASLP). This is superior to the integrated LPLNP (AUPR: 0.4584), the RWR (AUPR: 0.2827), the CF (AUPR: 0.2357), the LPIHN (AUPR: 0.2299), and the LPBNI (AUPR: 0.3302). It is very encouraging that most of the LPI predictions have been confirmed to be close to relevant concentrations.

Abstract Background In the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations. Considering the restrictions of previous computational methods for predicting potential miRNAs-disease associations, we develop the model of FKL-Spa-LapRLS (Fast Kernel Learning Sparse kernel Laplacian Regularized Least Squares) to break through the limitations. Result First, we extract three miRNA similarity kernels and three disease similarity kernels. Then, we combine these kernels into a single kernel through the Fast Kernel Learning (FKL) model, and use sparse kernel (Spa) to eliminate noise in the integrated similarity kernel. Finally, we find the associations via Laplacian Regularized Least Squares (LapRLS). Based on three evaluation methods, global and local leave-one-out cross validation (LOOCV), and 5-fold cross validation, the AUCs of our method achieve 0.9563, 0.8398 and 0.9535, thus it can be seen that our method is reliable. Then, we use case studies of eight neoplasms to further analyze the performance of our method. We find that most of the predicted miRNA-disease associations are confirmed by previous traditional experiments, and some important miRNAs should be paid more attention, which uncover more associations of various neoplasms than other miRNAs. Conclusions Our proposed model can reveal miRNA-disease associations and improve the accuracy of correlation prediction for various diseases. Our method can be also easily extended with more similarity kernels.

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are expensive and time-consuming. Recently, CPP predictors based on machine learning methods have become a research hotspot. Although considerable progress has been made, some challenges remain unresolved. First, most predictors employ a variety of feature descriptors to transform an original sequence into multiview data; however, extant methods ignore the relationships between different views, limiting further performance improvement. Second, most machine learning models are actually black boxes and cannot offer insightful advice. In this paper, a novel Hilbert–Schmidt independence criterion (HSIC)-based multiview TSK fuzzy system is proposed. Compared with other machine learning methods, TSK fuzzy systems have better interpretability, and the introduction of multiview mechanisms provides comprehensive insight into the intrinsic laws of the data. HSIC is utilized here to measure the independence and enhance the complementarity between different views. Notably, the proposed method attained prediction accuracy results of 92.2% and 96.2% for the training and independent test sets, respectively. The empirical results show that our promising approach features greater recognition performance than the state-of-the-art method.

Introduction: The electron transport chain is closely related to cellular respiration and has been implicated in various human diseases. However, the traditional “wet” experimental method is time consuming. Therefore, it is key to identify electron transport proteins by computational methods. Many approaches have been proposed, but performance of them still has room for further improvement. Methodological issues: In our study, we propose a model stacking framework, which combines multiple base models. The protein features are extracted via PsePSSM from protein sequences. Features are fed into the base model including support vector machines (SVM), random forest (RF), XGBoost, etc. The results of base model are entered into logistic regression model for final process. Results: On the independent dataset, the accuracy and Matthew’s correlation coefficient (MCC) of proposed method are 95.70% and 0.8756, respectively. Furthermore, we show that the model stacking framework outperforms single machine learning classifiers statistically. Conclusion: Our models are better than most known strategies for identifying electron transport proteins. Our model can be used to more precisely identify electron transport proteins.

Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised multiple kernel fusion method, Similarity Kernel Fusion (SKF), in order to integrate three similarity kernels into one combined kernel. Finally, we make use of spectral clustering on the integrated kernel to predict cancer subtypes. In the experimental results, the P-values from the Cox regression model and survival curve analysis can be used to evaluate the performance of predicted subtypes on three datasets. Our kernel fusion method, SKF, has outstanding performance compared with single kernel and other multiple kernel fusion strategies. It demonstrates that our method can accurately identify more accurate subtypes on various kinds of cancers. Our cancer subtype prediction method can identify essential genes and biomarkers for disease diagnosis and prognosis, and we also discuss the possible side effects of therapies and treatment.

Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins. They play an important role in regulating gene expression by interacting with the homologous RNA-binding proteins. Due to the laborious and time-consuming nature of wet experimental methods, more researchers should pay great attention to computational approaches for the prediction of lncRNA-protein interaction (LPI). An in-depth literature review in the state-of-the-art in silico investigations, leads to the conclusion that there is still room for improving the accuracy and velocity. This paper propose a novel method for identifying LPI by employing Kernel Ridge Regression, based on Fast Kernel Learning (LPI-FKLKRR). This approach, uses four distinct similarity measures for lncRNA and protein space, respectively. It is remarkable, that we extract Gene Ontology (GO) with proteins, in order to improve the quality of information in protein space. The process of heterogeneous kernels integration, applies Fast Kernel Learning (FastKL) to deal with weight optimization. The extrapolation model is obtained by gaining the ultimate prediction associations, after using Kernel Ridge Regression (KRR). Experimental outcomes show that the ability of modeling with LPI-FKLKRR has extraordinary performance compared with LPI prediction schemes. On benchmark dataset, it has been observed that the best Area Under Precision Recall Curve (AUPR) of 0.6950 is obtained by our proposed model LPI-FKLKRR, which outperforms the integrated LPLNP (AUPR: 0.4584), RWR (AUPR: 0.2827), CF (AUPR: 0.2357), LPIHN (AUPR: 0.2299), and LPBNI (AUPR: 0.3302). Also, combined with the experimental results of a case study on a novel dataset, it is anticipated that LPI-FKLKRR will be a useful tool for LPI prediction.

Identifying accurate associations between miRNAs and diseases is beneficial for diagnosis and treatment of human diseases. It is especially important to develop an efficient method to detect the association between miRNA and disease. Traditional experimental method has high precision, but its process is complicated and time-consuming. Various computational methods have been developed to uncover potential associations based on an assumption that similar miRNAs are always related to similar diseases. In this paper, we propose an accurate method, MDA-SKF, to uncover potential miRNA-disease associations. We first extract three miRNA similarity kernels (miRNA functional similarity, miRNA sequence similarity, Hamming profile similarity for miRNA) and three disease similarity kernels (disease semantic similarity, disease functional similarity, Hamming profile similarity for disease) in two subspaces, respectively. Then, due to limitations that some initial information may be lost in the process and some noises may be exist in integrated similarity kernel, we propose a novel Similarity Kernel Fusion (SKF) method to integrate multiple similarity kernels. Finally, we utilize the Laplacian Regularized Least Squares (LapRLS) method on the integrated kernel to find potential associations. MDA-SKF is evaluated by three evaluation methods, including global leave-one-out cross validation (LOOCV) and local LOOCV and 5-fold cross validation (CV), and achieves AUCs of 0.9576, 0.8356, and 0.9557, respectively. Compared with existing seven methods, MDA-SKF has outstanding performance on global LOOCV and 5-fold. We also test case studies to further analyze the performance of MDA-SKF on 32 diseases. Furthermore, 3200 candidate associations are obtained and a majority of them can be confirmed. It demonstrates that MDA-SKF is an accurate and efficient computational tool for guiding traditional experiments.

Nowadays a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. In the present work, we propose a method for predicting protein interactions making full use of physicochemical characteristics of amino acids. A protein sequence is encoded at multi-scale by seven properties, including their qualitative and quantitative descriptions, of amino acids. Five kinds of protein descriptors, frequency, composition, transformation, distribution and auto covariance, are extracted from these encodings for representing each protein sequence. The new formed feature representation consisted of 347 dimensions is able to capture not only the compositional and positional information but also their statistical significance of amino acids in the sequence. Based on such a feature representation, the gradient boosting decision tree algorithm is introduced to predict protein interaction class. When the proposed method is tested with the PPI data of S.cerevisiae, it achieves a prediction accuracy of 95.28% at the Matthew's correlation coefficient of 90.68%. Compared with the state-of-the-art works on H.pylori and Human, the accuracies can be raised to 89.27% and 98.00% respectively. Extensive experiments are performed for a crossover protein-protein interactions network and the prediction accuracies are also very promising. Because of learning capabilities of the gradient boosting decision tree and the mutil-scale feature representation scheme, the proposed method might be a useful tool for future proteomics studies.

Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix-Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix-Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at https://doi.org/10.6084/m9.figshare.5104084.

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm is employed to combine multiple features. Finally, a Fuzzy Kernel Ridge Regression (FKRR) model is built to detect DNA-binding proteins. Compared with other methods, our model achieves good results. Our method obtains an accuracy of 83.26% and 81.72% on two benchmark datasets (PDB1075 and compared with PDB186), respectively.

DNA–protein interactions appear as pivotal roles in diverse biological procedures and are paramount for cell metabolism, while identifying them with computational means is a kind of prudent scenario in depleting in vitro and in vivo experimental charging. A variety of state-of-the-art investigations have been elucidated to improve the accuracy of the DNA–protein binding sites prediction. Nevertheless, structure-based approaches are limited under the condition without 3D information, and the predictive validity is still refinable. In this essay, we address a kind of competitive method called Multi-scale Local Average Blocks (MLAB) algorithm to solve this issue. Different from structure-based routes, MLAB exploits a strategy that not only extracts local evolutionary information from primary sequences, but also using predicts solvent accessibility. Moreover, the construction about predictors of DNA–protein binding sites wields an ensemble weighted sparse representation model with random under-sampling. To evaluate the performance of MLAB, we conduct comprehensive experiments of DNA–protein binding sites prediction. MLAB gives M C C of 0.392 , 0.315 , 0.439 and 0.245 on PDNA-543, PDNA-41, PDNA-316 and PDNA-52 datasets, respectively. It shows that MLAB gains advantages by comparing with other outstanding methods. M C C for our method is increased by at least 0.053 , 0.015 and 0.064 on PDNA-543, PDNA-41 and PDNA-316 datasets, respectively.

The prediction of drug–target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug–target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns. Simultaneously, we apply the discrete wavelet transform (DWT) to extract features from target sequences. Then, we concatenate and normalize the target, drug, and network features to construct feature vectors. The prediction model is obtained by feeding these feature vectors into the support vector machine (SVM) classifier. Extensive experimental results show that the prediction ability of DAWN has a compatibility among other DTI prediction schemes. The prediction areas under the precision–recall curves (AUPRs) of four datasets are 0 . 895 (Enzyme), 0 . 921 (Ion Channel), 0 . 786 (guanosine-binding protein coupled receptor, GPCR), and 0 . 603 (Nuclear Receptor), respectively.

Identification of protein–protein interactions (PPIs) is a difficult and important problem in biology. Since experimental methods for predicting PPIs are both expensive and time-consuming, many computational methods have been developed to predict PPIs and interaction networks, which can be used to complement experimental approaches. However, these methods have limitations to overcome. They need a large number of homology proteins or literature to be applied in their method. In this paper, we propose a novel matrix-based protein sequence representation approach to predict PPIs, using an ensemble learning method for classification. We construct the matrix of Amino Acid Contact (AAC), based on the statistical analysis of residue-pairing frequencies in a database of 6323 protein–protein complexes. We first represent the protein sequence as a Substitution Matrix Representation (SMR) matrix. Then, the feature vector is extracted by applying algorithms of Histogram of Oriented Gradient (HOG) and Singular Value Decomposition (SVD) on the SMR matrix. Finally, we feed the feature vector into a Random Forest (RF) for judging interaction pairs and non-interaction pairs. Our method is applied to several PPI datasets to evaluate its performance. On the S . c e r e v i s i a e dataset, our method achieves 94 . 83 % accuracy and 92 . 40 % sensitivity. Compared with existing methods, and the accuracy of our method is increased by 0 . 11 percentage points. On the H . p y l o r i dataset, our method achieves 89 . 06 % accuracy and 88 . 15 % sensitivity, the accuracy of our method is increased by 0 . 76 % . On the H u m a n PPI dataset, our method achieves 97 . 60 % accuracy and 96 . 37 % sensitivity, and the accuracy of our method is increased by 1 . 30 % . In addition, we test our method on a very important PPI network, and it achieves 92 . 71 % accuracy. In the Wnt-related network, the accuracy of our method is increased by 16 . 67 % . The source code and all datasets are available at https://figshare.com/s/580c11dce13e63cb9a53.

Introduction: Si-Ni-San (SNS), a traditional Chinese medicine, is effective in treating liver fibrosis with an unclear mechanism. Although disturbance of intestinal flora and the subsequent secretion of short-chain fatty acids (SCFAs) is suggested to be involved in the progression of liver fibrosis, whether SNS produces the anti-fibrosis effect through the regulation of intestinal flora and SCFAs remains unclear. Methods: In the current study, carbon tetrachloride (CCl4)-treated mice were dosed with SNS to examine the anti-fibrotic effects and the involved mechanism. Biochemical parameters, histological staining, and analyses of fibrotic gene expression were used to evaluate the anti-fibrotic effect of SNS, while intestinal flora and SCFA content were determined by 16S rRNA and LC-MS to evaluate the mechanism. Results: In vivo results showed that SNS improved liver function, reduced hepatocyte apoptosis and FFAR2/3 expression, and restored intestinal dysbiosis and reduced PA, BA, and IsA levels. In vitro experiments showed that PA, BA, and IsA exacerbated TNF-α-induced HepG2 apoptosis. Notably, the protective effects of SNS were compromised in pseudo-sterile mice. Discussion: In conclusion, our experimental results suggest that the disturbance in intestinal flora results in elevated SCFA levels, which further exacerbates hepatocyte apoptosis in liver fibrosis, while SNS suppresses CCl4-induced liver fibrosis at least partially by reinstating intestinal flora homeostasis and reducing SCFA levels. [ABSTRACT FROM AUTHOR]