LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

MOTIVATION: There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. RESULTS: In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION: The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features.

Circular RNAs (circRNAs) are reverse-spliced and covalently closed RNAs. Their interactions with RNA-binding proteins (RBPs) have multiple effects on the progress of many diseases. Some computational methods are proposed to identify RBP binding sites on circRNAs but suffer from insufficient accuracy, robustness and explanation. In this study, we first take the characteristics of both RNA and RBP into consideration. We propose a method for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features, called CRMSS. For circRNAs, we use sequence ${k}\hbox{-}{mer}$ embedding and the forming probabilities of local secondary structures as features. For RBPs, we combine sequence and structure frequencies of RNA-binding domain regions to generate features. We capture binding patterns with multi-scale residual blocks. With BiLSTM and attention mechanism, we obtain the contextual information of high-level representation for circRNA-RBP binding. To validate the effectiveness of CRMSS, we compare its predictive performance with other methods on 37 RBPs. Taking the properties of both circRNAs and RBPs into account, CRMSS achieves superior performance over state-of-the-art methods. In the case study, our model provides reliable predictions and correctly identifies experimentally verified circRNA-RBP pairs. The code of CRMSS is freely available at https://github.com/BioinformaticsCSU/CRMSS.

GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation.

The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding lncRNA functions. Most of existing lncRNA subcellular localization prediction methods use k-mer frequency features to encode lncRNA sequences. However, k-mer frequency features lose sequence order information and fail to capture sequence patterns and motifs of different lengths. In this paper, we proposed GraphLncLoc, a graph convolutional network-based deep learning model, for predicting lncRNA subcellular localization. Unlike previous studies encoding lncRNA sequences by using k-mer frequency features, GraphLncLoc transforms lncRNA sequences into de Bruijn graphs, which transforms the sequence classification problem into a graph classification problem. To extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to perform the prediction task. Extensive experiments show that GraphLncLoc achieves better performance than traditional machine learning models and existing predictors. In addition, our analyses show that transforming sequences into graphs has more distinguishable features and is more robust than k-mer frequency features. The case study shows that GraphLncLoc can uncover important motifs for nucleus subcellular localization. GraphLncLoc web server is available at http://csuligroup.com:8000/GraphLncLoc/.

Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.

Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding.

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.

Deep Matrix Factorization Improves Prediction of Human CircRNA-Disease Associations.

In recent years, more and more evidence indicates that circular RNAs (circRNAs) with covalently closed loop play various roles in biological processes. Dysregulation and mutation of circRNAs may be implicated in diseases. Due to its stable structure and resistance to degradation, circRNAs provide great potential to be diagnostic biomarkers. Therefore, predicting circRNA-disease associations is helpful in disease diagnosis. However, there are few experimentally validated associations between circRNAs and diseases. Although several computational methods have been proposed, precisely representing underlying features and grasping the complex structures of data are still challenging. In this paper, we design a new method, called DMFCDA (Deep Matrix Factorization CircRNA-Disease Association), to infer potential circRNA-disease associations. DMFCDA takes both explicit and implicit feedback into account. Then, it uses a projection layer to automatically learn latent representations of circRNAs and diseases. With multi-layer neural networks, DMFCDA can model the non-linear associations to grasp the complex structure of data. We assess the performance of DMFCDA using leave-one cross-validation and 5-fold cross-validation on two datasets. Computational results show that DMFCDA efficiently infers circRNA-disease associations according to AUC values, the percentage of precisely retrieved associations in various top ranks, and statistical comparison. We also conduct case studies to evaluate DMFCDA. All results show that DMFCDA provides accurate predictions.

DMFLDA: A Deep Learning Framework for Predicting lncRNA-Disease Associations.

A growing amount of evidence suggests that long non-coding RNAs (lncRNAs) play important roles in the regulation of biological processes in many human diseases. However, the number of experimentally verified lncRNA-disease associations is very limited. Thus, various computational approaches are proposed to predict lncRNA-disease associations. Current matrix factorization-based methods cannot capture the complex non-linear relationship between lncRNAs and diseases, and traditional machine learning-based methods are not sufficiently powerful to learn the representation of lncRNAs and diseases. Considering these limitations in existing computational methods, we propose a deep matrix factorization model to predict lncRNA-disease associations (DMFLDA in short). DMFLDA uses a cascade of non-linear hidden layers to learn latent representation to represent lncRNAs and diseases. By using non-linear hidden layers, DMFLDA captures the more complex non-linear relationship between lncRNAs and diseases than traditional matrix factorization-based methods. In addition, DMFLDA learns features directly from the lncRNA-disease interaction matrix and thus can obtain more accurate representation learning for lncRNAs and diseases than traditional machine learning methods. The low dimensional representations of the lncRNAs and diseases are fused to estimate the new interaction value. To evaluate the performance of DMFLDA, we perform leave-one-out cross-validation and 5-fold cross-validation on known experimentally verified lncRNA-disease associations. The experimental results show that DMFLDA performs better than the existing methods. The case studies show that many predicted interactions of colorectal cancer, prostate cancer, and renal cancer have been verified by recent biomedical literature. The source code and datasets can be obtained from https://github.com/CSUBioGroup/DMFLDA.

Heterogeneous graph inference with matrix completion for computational drug repositioning.

MOTIVATION: Emerging evidence presents that traditional drug discovery experiment is time-consuming and high costs. Computational drug repositioning plays a critical role in saving time and resources for drug research and discovery. Therefore, developing more accurate and efficient approaches is imperative. Heterogeneous graph inference is a classical method in computational drug repositioning, which not only has high convergence precision, but also has fast convergence speed. However, the method has not fully considered the sparsity of heterogeneous association network. In addition, rough similarity measure can reduce the performance in identifying drug-associated indications. RESULTS: In this article, we propose a heterogeneous graph inference with matrix completion (HGIMC) method to predict potential indications for approved and novel drugs. First, we use a bounded matrix completion (BMC) model to prefill a part of the missing entries in original drug-disease association matrix. This step can add more positive and formative drug-disease edges between drug network and disease network. Second, Gaussian radial basis function (GRB) is employed to improve the drug and disease similarities since the performance of heterogeneous graph inference more relies on similarity measures. Next, based on the updated drug-disease associations and new similarity measures of drug and disease, we construct a novel heterogeneous drug-disease network. Finally, HGIMC utilizes the heterogeneous network to infer the scores of unknown association pairs, and then recommend the promising indications for drugs. To evaluate the performance of our method, HGIMC is compared with five state-of-the-art approaches of drug repositioning in the 10-fold cross-validation and de novo tests. As the numerical results shown, HGIMC not only achieves a better prediction performance but also has an excellent computation efficiency. In addition, cases studies also confirm the effectiveness of our method in practical application. AVAILABILITYAND IMPLEMENTATION: The HGIMC software and data are freely available at https://github.com/BioinformaticsCSU/HGIMC, https://hub.docker.com/repository/docker/yangmy84/hgimc and http://doi.org/10.5281/zenodo.4285640. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving circRNA-disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks.

MOTIVATION: Emerging studies indicate that circular RNAs (circRNAs) are widely involved in the progression of human diseases. Due to its special structure which is stable, circRNAs are promising diagnostic and prognostic biomarkers for diseases. However, the experimental verification of circRNA-disease associations is expensive and limited to small-scale. Effective computational methods for predicting potential circRNA-disease associations are regarded as a matter of urgency. Although several models have been proposed, over-reliance on known associations and the absence of characteristics of biological functions make precise predictions are still challenging. RESULTS: In this study, we propose a method for predicting CircRNA-disease associations based on sequence and ontology representations, named CDASOR, with convolutional and recurrent neural networks. For sequences of circRNAs, we encode them with continuous k-mers, get low-dimensional vectors of k-mers, extract their local feature vectors with 1D CNN and learn their long-term dependencies with bi-directional long short-term memory. For diseases, we serialize disease ontology into sentences containing the hierarchy of ontology, obtain low-dimensional vectors for disease ontology terms and get terms' dependencies. Furthermore, we get association patterns of circRNAs and diseases from known circRNA-disease associations with neural networks. After the above steps, we get circRNAs' and diseases' high-level representations, which are informative to improve the prediction. The experimental results show that CDASOR provides an accurate prediction. Importing the characteristics of biological functions, CDASOR achieves impressive predictions in the de novo test. In addition, 6 of the top-10 predicted results are verified by the published literature in the case studies. AVAILABILITY AND IMPLEMENTATION: The code and data of CDASOR are freely available at https://github.com/BioinformaticsCSU/CDASOR.

SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning.

In recent years, accumulating studies have shown that long non-coding RNAs (lncRNAs) not only play an important role in the regulation of various biological processes but also are the foundation for understanding mechanisms of human diseases. Due to the high cost of traditional biological experiments, the number of experimentally verified lncRNA-disease associations is very limited. Thus, many computational approaches have been proposed to discover the underlying associations between lncRNAs and diseases. However, the associations between lncRNAs and diseases are too complicated to model by using only traditional matrix factorization-based methods. In this study, we propose a hybrid computational framework (SDLDA) for the lncRNA-disease association prediction. In our computational framework, we use singular value decomposition and deep learning to extract linear and non-linear features of lncRNAs and diseases, respectively. Then we train SDLDA by combing the linear and non-linear features. Compared to previous computational methods, the combination of linear and non-linear features reinforces each other, which is better than using only either matrix factorization or deep learning. The computational results show that SDLDA has a better performance over existing methods in the leave-one-out cross-validation. Furthermore, the case studies show that 28 out of 30 cancer-related lncRNAs (10 for gastric cancer, 10 for colon cancer and 8 for renal cancer) are verified by mining recent biomedical literature. Code and data can be accessed at https://github.com/CSUBioGroup/SDLDA.

Predicting Human lncRNA-Disease Associations Based on Geometric Matrix Completion.

Recently, increasing evidences reveal that dysregulations of long non-coding RNAs (lncRNAs) are relevant to diverse diseases. However, the number of experimentally verified lncRNA-disease associations is limited. Prioritizing potential associations is beneficial not only for disease diagnosis, but also disease treatment, more important apprehending disease mechanisms at lncRNA level. Various computational methods have been proposed, but precise prediction and full use of data's intrinsic structure are still challenging. In this work, we design a new method, denominated GMCLDA (Geometric Matrix Completion lncRNA-Disease Association), to infer underlying associations based on geometric matrix completion. Utilizing association patterns among functionally similar lncRNAs and phenotypically similar diseases, GMCLCA makes use of the intrinsic structure embedded in the association matrix. Besides, limiting the scope of the predicted values gives rise to a certain sparsity in computation and enhances the robustness of GMCLDA. GMCLDA computes disease semantic similarity according to the Disease Ontology (DO) hierarchy and lncRNA Gaussian interaction profile kernel similarity according to known interaction profiles. Then, GMCLDA measures lncRNA sequence similarity using Needleman-Wunsch algorithm. For a new lncRNA, GMCLDA prefills interaction profile on account of its K-nearest neighbors defined by sequence similarity. Finally, GMCLDA estimates the missing entries of the association matrix based on geometric matrix completion model. Compared with state-of-the-art methods, GMCLDA can provide more accurate lncRNA-disease prediction. Further case studies prove that GMCLDA is able to correctly infer possible lncRNAs for renal cancer.

Prediction of lncRNA-disease associations based on inductive matrix completion.

Motivation: Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA-disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA-disease associations, especially for novel lncRNAs, remains challenging. Results: In this study, we propose a method (named SIMCLDA) for predicting potential lncRNA-disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA-disease interactions and functional similarity of diseases based on disease-gene and gene-gene onotology associations. Then, we extract primary feature vectors from Gaussian interaction profile kernel of lncRNAs and functional similarity of diseases by principal component analysis, respectively. For a new lncRNA, we calculate the interaction profile according to the interaction profiles of its neighbors. At last, we complete the association matrix based on the inductive matrix completion framework using the primary feature vectors from the constructed feature matrices. Computational results show that SIMCLDA can effectively predict lncRNA-disease associations with higher accuracy compared with previous methods. Furthermore, case studies show that SIMCLDA can effectively predict candidate lncRNAs for renal cancer, gastric cancer and prostate cancer. Availability and implementation: https://github.com//bioinfomaticsCSU/SIMCLDA. Supplementary information: Supplementary data are available at Bioinformatics online.

STAT3 and beta-catenin signaling pathway may affect GSK-3beta expression in hepatocellular carcinoma.

BACKGROUND/AIMS: To study the correlation and significance of beta-catenin, STAT3 and GSK-3beta signaling pathway in hepatocellular carcinoma (HCC). METHODOLOGY: The HCC cell line HepG2 was transfected with small interfering RNA (siRNA) directed against 8-catenin or STAT3. After 72 and 96h, protein was extracted and the protein expression of beta-catenin, STAT3, and GSK-3beta was detected by Western blot analysis. RESULTS: After siRNA directed against beta-catenin was transfected into HepG2 cells, beta-catenin protein expression was decreased at 72 and 96h, GSK-3beta and p-GSK-3beta protein expression increased gradually at 72 and 96h, and STAT3 protein expression showed no change following transfection. After siRNA directed against STAT3 was transfected into HepG2 cells, STAT3 protein expression was decreased at 72 and 96h and beta-catenin, GSK-3beta and p-GSK-3beta protein expression all increased at 72h and decreased at 96 h after transfection. CONCLUSION: In HCC, the beta-catenin signaling pathway may regulate GSK-3beta protein expression and the STAT3 signaling pathway may regulate beta-catenin and GSK-3beta protein expression, thereby playing key roles during HCC genesis and development.

NaFeEDTA fortified soy sauce showed higher iron absorption rate in Chinese females.

OBJECTIVE: NaFeEDTA was considered as a promising iron fortificant for controlling iron deficiency anemia. Soy sauce is a suitable food carrier for iron fortification and is a popular condiment in China. Iron absorption rates of NaFeEDTA and FeSO4 were observed and compared in adult female subjects. METHODS: The stable isotope tracer method was used in Chinese females consuming a typical Chinese diet. Ten healthy young Chinese women were selected as subjects in the 15-day study. A plant-based diet was used based on the dietary pattern of adult women in the 1992 National Nutrition Survey. Six milligram of 54Fe in 54FeSO4 soy sauce and 3 mg 58Fe in Na58FeEDTA soy sauce were given to the same subjects in two days. Food samples and fecal samples were collected and analyzed. RESULTS: Iron absorption rates of NaFeEDTA and FeSO4 were 10.51% +/- 2.83 and 4.73% +/- 2.15 respectively. The 58Fe (NaFeEDTA) absorption was significantly higher than that of 54Fe (FeSO4) (P < 0.01). The iron absorption rate from NaFeEDTA was 1.2 times higher than that from FeSO4 in Chinese adult women consuming a typical Chinese diet. CONCLUSION: The higher absorption rate of NaFeEDTA suggested that NaFeEDTA would be a better iron fortificant used in soy sauce for the controlling of iron deficiency anemia in China.

[Correlation comparison of uterotrophic assay and E-SCREEN assay for estrogenic activities].

OBJECTIVE: To evaluate the correlation of uterotrophic assay in immature mice and E-SCREEN assay for estrogenic activities using 17beta-estradiol (17beta-E2) and four botanical extracts (Fructus Foeniculi, Fructus Psoraleae, Radix Cyathulae and Radix Sophorae Tonkinensis). METHODS: Weanling female Kunming mice weighed 10 - 12 g were given botanical extracts (10 g/kg BW, i.g) and 17beta-E2 (0.5 mg/kg BW, s.c), respectively, for 9 days. Uterine and ovary weights of mice were measured after killed. Human breast cancer MCF-7 cells were cultured and treated with botanical extracts (final concentration: 10 mg/L) and 17beta-E2 (final concentration: 0.3 micromol/L) for 120 h, respectively. The doubling time of cell growth was calculated and analyzed its correlation with average uterine weights obtained from uterotrophic assay. Results Four botanical extracts, as well as 17beta-E2, increased uterine weights of mice significantly (P < 0.05 or P < 0.01) and shortened the doubling time of cell growth. And uterine weights were inversely correlated with the doubling time of cell growth (r = -0.967, P < 0.01). CONCLUSION: The result of uterotrophic assay in immature mice was consistent with that of E-SCREEN assay when evaluating estrogenic activities.

[Factors of antioxidant and prooxidant activities of carotenoids].

The effectiveness of carotenoids as antioxidants is dependent on a number of influencing factors. It is likely that carotenoids exhibit a tendency to lose their effectiveness as antioxidants or act as prooxidants. In this paper, factors influencing the antioxidant or prooxidant activities of carotenoids such as the molecule structure of carotenoids, the location or site of action of the carotenoid molecule within the cell, the concentration of carotenoids, the properties of reactants, the partial pressure of oxygen and the interaction with other antioxidants were reviewed.

Therapeutic effects of NaFeEDTA-fortified soy sauce in anaemic children in China.

The therapeutic effects of NaFeEDTA-fortified soy sauce on anaemic students were investigated. Three hundred and four iron-deficient anaemic school children (11-17 years) were randomly assigned to three treatment groups: control group (consuming non-fortified soy sauce), low-NaFeEDTA group (consuming fortified soy sauce, providing 5 mg Fe/day) and high-NaFeEDTA group (consuming fortified soy sauce, providing 20 mg Fe/day). Blood haemoglobin (Hb) levels were determined before and after 1 month, 2 months and 3 months of intervention. In addition, serum iron (SI), serum ferritin (SF), free erythrocytic porphyrin (FEP), total iron binding capability (TIBC) and transferritin (TF) were measured before and after consumption of soy sauce for 3 months. The results obtained herein show that the parameters measured were not changed remarkably within the 3-month intervention in the control group (P < 0.05). However, increased Hb, SI, SF and TF levels and decreased TIBC and FEP levels were observed in both the high-NaFeEDTA group (P <0.01) and the low-NaFeEDTA group (P < 0.05). The effectiveness of iron intervention in the low-NaFeEDTA group and high-NaFeEDTA group had no statistical significance after 3 months. It was concluded that nutritional intervention for anaemic students using NaFeEDTA-fortified soy sauce could play a positive role in the improvement of iron status and control of anaemia.