Abstract
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.
Abstract
With the development of high-throughput technology and the accumulation of biomedical data, the prior information of biological entity can be calculated from different aspects. Specifically, ...drug–drug similarities can be measured from target profiles, drug–drug interaction and side effects. Similarly, different methods and data sources to calculate disease ontology can result in multiple measures of pairwise disease similarities. Therefore, in computational drug repositioning, developing a dynamic method to optimize the fusion process of multiple similarities is a crucial and challenging task. In this study, we propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-associated indications for existing and novel drugs. Instead of fusing multiple similarities into a single similarity matrix, we concatenate these similarity matrices of drug and disease, respectively. Applying matrix factorization methods, we decompose the drug–disease association matrix into a drug-feature matrix and a disease-feature matrix. At the same time, using these feature matrices as basis, we extract effective latent features representing the drug and disease similarity matrices to infer missing drug–disease associations. Moreover, these two factored matrices are constrained by non-negative factorization to ensure that the completed drug–disease association matrix is biologically interpretable. In addition, we numerically solve the MSBMF model by an efficient alternating direction method of multipliers algorithm. The computational experiment results show that MSBMF obtains higher prediction accuracy than the state-of-the-art drug repositioning methods in cross-validation experiments. Case studies also demonstrate the effectiveness of our proposed method in practical applications. Availability: The data and code of MSBMF are freely available at https://github.com/BioinformaticsCSU/MSBMF. Corresponding author: Jianxin Wang, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. E-mail: jxwang@mail.csu.edu.cn Supplementary Data: Supplementary data are available online at https://academic.oup.com/bib.
Abstract
Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the ...rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions.
Incorporation of carbon nanotubes (CNTs) into textiles without sacrificing their intrinsic properties provides a promising platform in exploring wearable technology. However, manufacture of flexible, ...durable, and stretchable CNT/textile composites on an industrial scale is still a great challenge. We hereby report a facile way of incorporating CNTs into the traditional yarn manufacturing process by dipping and drying CNTs into cotton rovings followed by fabricating CNT/cotton/spandex composite yarn (CCSCY) in sirofil spinning. The existence of CNTs in CCSCY brings electrical conductivity to CCSCY while the mechanical properties and stretchability are preserved. We demonstrate that the CCSCY can be used as wearable strain sensors, exhibiting ultrahigh strain sensing range, excellent stability, and good washing durability. Furthermore, CCSCY can be used to accurately monitor the real-time human motions, such as leg bending, walking, finger bending, wrist activity, clenching fist, bending down, and pronouncing words. We also demonstrate that the CCSCY can be assembled into knitted fabrics as the conductors with electric heating performance. The reported manufacturing technology of CCSCY could lead to an industrial-scale development of e-textiles for wearable applications.
Motivation The aim of this study was to construct relationships among drug, targets and diseases. To represent the complex relationships among these entities, we used a heterogeneous graph structure. ...Additionally, we propose a DTD-GNN model that combines graph convolutional networks and graph attention networks to learn feature representations and association information, facilitating a more thorough exploration of the relationships. The experimental results demonstrate that the DTD-GNN model outperforms other graph neural network models in terms of AUC, Precision, and F1-score. The study has important implications for gaining a comprehensive understanding of the relationships between drugs and diseases, as well as for further research and application in exploring the mechanisms of drug-disease interactions. The study reveals these relationships, providing possibilities for innovative therapeutic strategies in medicine.
Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease ...similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications.
Background:
Drug repositioning is considered a promising drug development strategy with the goal of discovering new uses for existing drugs. Compared with the experimental screening for drug ...discovery, computational drug repositioning offers lower cost and higher efficiency and, hence, has become a hot issue in bioinformatics. However, there are sparse samples, multi-source information, and even some noises, which makes it difficult to accurately identify potential drug-associated indications.
Methods:
In this article, we propose a new scheme with improved tensor robust principal component analysis (ITRPCA) in multi-source data to predict promising drug–disease associations. First, we use a weighted
k
-nearest neighbor (WKNN) approach to increase the overall density of the drug–disease association matrix that will assist in prediction. Second, a drug tensor with five frontal slices and a disease tensor with two frontal slices are constructed using multi-similarity matrices and an updated association matrix. The two target tensors naturally integrate multiple sources of data from the drug-side aspect and the disease-side aspect, respectively. Third, ITRPCA is employed to isolate the low-rank tensor and noise information in the tensor. In this step, an additional range constraint is incorporated to ensure that all the predicted entry values of a low-rank tensor are within the specific interval. Finally, we focus on identifying promising drug indications by analyzing drug–disease association pairs derived from the low-rank drug and low-rank disease tensors.
Results:
We evaluate the effectiveness of the ITRPCA method by comparing it with five prominent existing drug repositioning methods. This evaluation is carried out using 10-fold cross-validation and independent testing experiments. Our numerical results show that ITRPCA not only yields higher prediction accuracy but also exhibits remarkable computational efficiency. Furthermore, case studies demonstrate the practical effectiveness of our method.
Conductive cotton fabric was prepared by coating single-wall carbon nanotubes (CNTs) on a knitted cotton fabric surface through a "dip-and-dry" method. The combination of CNTs and cotton fabric was ...analyzed using scanning electron microscopy (SEM) and Raman scattering spectroscopy. The CNTs coating improved the mechanical properties of the fabric and imparted conductivity to the fabric. The electromechanical performance of the CNT-cotton fabric (CCF) was evaluated. Strain sensors made from the CCF exhibited a large workable strain range (0~100%), fast response and great stability. Furthermore, CCF-based strain sensors was used to monitor the real-time human motions, such as standing, walking, running, squatting and bending of finger and elbow. The CCF also exhibited strong electric heating effect. The flexible strain sensors and electric heaters made from CCF have potential applications in wearable electronic devices and cold weather conditions.
A facile and original method was developed to fabricate flexible conductive yarns using cotton roving and carbon nanotubes (CNTs). The CNTs were assembled to cotton roving and then wrapped around by ...fibers through twisting during ring spinning. The obtained CNT treated cotton yarns (CNT-CYs) showed great electrical conductivity and durability properties. The CNT-CYs were analyzed using scanning electron microscopy and Raman scattering spectroscopy. The electrical conductivity, mechanical property and flexibility of CNT-CYs were investigated. The results show that electrical resistance of roving, twist and linear density of yarn affect the electrical conductivity of CNT-CYs. Combination with CNTs increased the breaking strength of cotton yarns. The electrical resistance of CNT-CYs was relatively stable during stretching and human motions. Moreover, no obvious changes in electrical resistance were found after CNT-CYs were bent 100 times. The CNT-CYs possessed good durability to repeated washing and abrasion.
PDF
