lncRNA-Disease Association Prediction Based On Weight Matrix And Projection Score

2021 ◽  
Author(s):  
Bo Wang ◽  
Chao Zhang ◽  
Xiao-xin Du ◽  
Jian-fei Zhang
Keyword(s):  
Semantic Similarity ◽  
Biological Activities ◽  
Medical Science ◽  
Disease Association ◽  
Projection Algorithm ◽  
Good Prediction ◽  
Similarity Matrix ◽  
Disease Similarity ◽  
Simulation Results ◽  
Projection Score

Abstract Background: with the development of medical science, lncRNA, originally considered as a noise gene, has been found to participate in a variety of biological activities. Nowadays, more and more studies show that lncRNA is involved in various human diseases, such as gastric cancer, prostate cancer, lung cancer, etc. However, obtaining lncRNA-disease association only through biological experiments not only costs manpower and material resources, but also gains little. Therefore, it is very important to develop effective computational models for predicting lncRNA-disease association. Results: In this paper, a new lncRNA-disease association prediction model LDAP-WMPS based on weight distribution and projection score is proposed. Based on the existing research results of disease semantic similarity, the integrated lncRNA similarity matrix and the integrated disease similarity matrix are calculated according to the disease semantic similarity and the association information between data. On this basis, the weight algorithm is combined with the improved projection algorithm to predict the lncRNA-disease association through the known lncRNA-miRNA association and miRNA-disease association. The simulation results show that under the loocv framework, the AUC of LDAP-WMPS can reach 0.8822. Better than the latest results. Through the case study of adenocarcinoma and colorectal cancer, it is proved that LDAP-WMPS can effectively infer lncRNA-disease association. Conclusions: The simulation results show that LDAP-WMPS has good prediction performance, which is an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease association data. Keywords: lncRNA-miRNA association, miRNA-disease association, disease semantic similarity, Integrated lncRNA similarity, integrated disease similarity, Weight allocation algorithm, Projection score.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

scholarly journals Mutravirechaniya mahakashaya as a solution towards urinary disorders – A compilation on research-based evaluation

2020 ◽  
Vol 6 (3) ◽  
pp. 194-201
Author(s):  
Darshana Deka ◽  
Keyword(s):  
Biological Activities ◽  
Fluid Collection ◽  
Medical Science ◽  
Urinary Calculi ◽  
Renal Calculi ◽  
Treatment Modalities ◽  
Renal Diseases ◽  
Individual Plant ◽  
Urinary Flow ◽  
Traditional System

Renal disorders are growing very rapidly among people all over the world nowadays and the treatment modalities available in modern medicine have undesirable side effects on human health. Plants of mutravirechaniya mahakashaya, described as, 35th mahakashaya in the 4th chapter of Charaka Samhita, Purvardha are mostly recognised for their urine inducing or urinary flow increasing capacity along with urinary system defending property in the ancient ayurvedic medical science. Formulations containing these plants as main ingredients have been regularly prescribed for the cases of abdominal fluid collection, renal problems, renal calculi, fluid collection in the lower extremities or any other cases of fluid overload in traditional system of Indian medicine. Studies approving urinary flow enhancing capacity along with the urinary calculi destroying property for the active ingredient of the individual plant, explain these plants’ utilization for renal diseases. Induction of adequate urine output is the basic concept of treatment for these disorders as majority of these conditions hamper normal filtration mechanism of the excretory system. Current article tries to specify the research works done scientifically upon the herbs having diuretic properties grouped together under the roof of mutravirechaniya mahakashaya in ayurvedic classics. It is composed of the knowledge gained from various scholarly articles, scientific papers, books and research topics gathered through the medium of documentation and internet. The presented compilation work helps towards proving its biological activities and pharmacology of its extracts which will contribute towards further exploration of this group of great clinical potential. However, further studies should be carried out to identify the mechanism of the pharmacological actions of these drugs classically mentioned in a group of diuretics.

scholarly journals A Machine Learning Framework that Integrates Multi-omics Data Predicts Cancer-related LncRNAs

2021 ◽  
Author(s):  
Lin Yuan ◽  
Jing Zhao ◽  
Tao Sun ◽  
Zhen Shen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Disease Association ◽  
Interaction Matrix ◽  
Omics Data ◽  
Similarity Matrix ◽  
Lncrna Gene ◽  
Neighborhood Information ◽  
Learning Framework ◽  
Association Matrix

Abstract Background: LncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases. Results: In this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively. LGDLDA calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. LGDLDA obtains gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix. LGDLDA obtains disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. Conclusions: Compared with lncRNA-disease prediction methods, IHI-BMLLR takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied LGDLDA to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.

Carrageenan: A Wonder Polymer from Marine Algae for Potential Drug Delivery Applications

2019 ◽  
Vol 25 (11) ◽  
pp. 1172-1186 ◽  
Author(s):  
Dilshad Qureshi ◽  
Suraj Kumar Nayak ◽  
Samarendra Maji ◽  
Doman Kim ◽  
Indranil Banerjee ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Biological Activities ◽  
Medical Science ◽  
Drug Carriers ◽  
Sulfated Polysaccharides ◽  
Potential Drug ◽  
Natural Polymers ◽  
Structural Variations ◽  
Varied Range ◽  
Drug Delivery Applications

Background: With the advancement in the field of medical science, the idea of sustained release of the therapeutic agents in the patient’s body has remained a major thrust for developing advanced drug delivery systems (DDSs). The critical requirement for fabricating these DDSs is to facilitate the delivery of their cargos in a spatio-temporal and pharmacokinetically-controlled manner. Albeit the synthetic polymer-based DDSs normally address the above-mentioned conditions, their potential cytotoxicity and high cost have ultimately constrained their success. Consequently, the utilization of natural polymers for the fabrication of tunable DDSs owing to their biocompatible, biodegradable, and non-toxic nature can be regarded as a significant stride in the field of drug delivery. Marine environment serves as an untapped resource of varied range of materials such as polysaccharides, which can easily be utilized for developing various DDSs. Methods: Carrageenans are the sulfated polysaccharides that are extracted from the cell wall of red seaweeds. They exhibit an assimilation of various biological activities such as anti-thrombotic, anti-viral, anticancer, and immunomodulatory properties. The main aim of the presented review is threefold. The first one is to describe the unique physicochemical properties and structural composition of different types of carrageenans. The second is to illustrate the preparation methods of the different carrageenan-based macro- and micro-dimensional DDSs like hydrogels, microparticles, and microspheres respectively. Fabrication techniques of some advanced DDSs such as floating hydrogels, aerogels, and 3-D printed hydrogels have also been discussed in this review. Next, considerable attention has been paid to list down the recent applications of carrageenan-based polymeric architectures in the field of drug delivery. Results: Presence of structural variations among the different carrageenan types helps in regulating their temperature and ion-dependent sol-to-gel transition behavior. The constraint of low mechanical strength of reversible gels can be easily eradicated using chemical crosslinking techniques. Carrageenan based-microdimesional DDSs (e.g. microspheres, microparticles) can be utilized for easy and controlled drug administration. Moreover, carrageenans can be fabricated as 3-D printed hydrogels, floating hydrogels, and aerogels for controlled drug delivery applications. Conclusion: In order to address the problems associated with many of the available DDSs, carrageenans are establishing their worth recently as potential drug carriers owing to their varied range of properties. Different architectures of carrageenans are currently being explored as advanced DDSs. In the near future, translation of carrageenan-based advanced DDSs in the clinical applications seems inevitable.

scholarly journals Finding disease similarity based on implicit semantic similarity

2012 ◽  
Vol 45 (2) ◽  
pp. 363-371 ◽  
Author(s):  
Sachin Mathur ◽  
Deendayal Dinakarpandian
Keyword(s):  
Semantic Similarity ◽  
Disease Similarity

scholarly journals Affine Projection Algorithm Using Regressive Estimated Error

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Shu Zhang ◽  
Yongfeng Zhi
Keyword(s):  
Convergence Rate ◽  
Adaptive Filtering ◽  
Projection Algorithm ◽  
Error Signal ◽  
Affine Projection ◽  
Affine Projection Algorithm ◽  
Fast Convergence Rate ◽  
Simulation Results ◽  
The Stability ◽  
Estimated Error

An affine projection algorithm using regressive estimated error (APA-REE) is presented in this paper. By redefining the iterated error of the affine projection algorithm (APA), a new algorithm is obtained, and it improves the adaptive filtering convergence rate. We analyze the iterated error signal and the stability for the APA-REE algorithm. The steady-state weights of the APA-REE algorithm are proved to be unbiased and consist. The simulation results show that the proposed algorithm has a fast convergence rate compared with the APA algorithm.

scholarly journals The Economic Analysis on the Competitiveness of Small Towns with Sports Characteristics under the Guidance of “Diamond Model” Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yan Xu
Keyword(s):  
Economic Development ◽  
Model Theory ◽  
Development Trend ◽  
Small Towns ◽  
Good Prediction ◽  
Matlab Simulation ◽  
Sports Industry ◽  
Diamond Model ◽  
Simulation Results ◽  
Production Demand

The sunrise industry of sports characteristic town under the transformation of economic development and the change of social demand contradiction is also a new growth point of sports industry economy. If we avoid homogeneous competition and improve the comprehensive competitiveness of small towns with sports characteristics, it will be a hot issue studied by scholars at home and abroad. The “Diamond Model” theory finds out the shortcomings of the development of sports characteristic towns by analyzing the factors of production, demand conditions, relevant supporting industries, and development strategies and puts forward the corresponding countermeasures. MATLAB simulation results show that the “Diamond Model” theory can improve the comprehensive competitiveness of sports characteristic towns, reduce the consumption of existing resources, and make a good prediction of the future development trend. Therefore, Porter’s “Diamond Model” theory can provide guidance for the improvement of the comprehensive competitiveness of sports characteristic towns, and this study can provide theoretical and case support for relevant domestic research.

scholarly journals A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association

2019 ◽  
Vol 20 (7) ◽  
pp. 1549 ◽  
Author(s):  
Yang Liu ◽  
Xiang Feng ◽  
Haochen Zhao ◽  
Zhanwei Xuan ◽  
Lei Wang
Keyword(s):  
Computational Models ◽  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Weighted Matrix ◽  
Disease Associations ◽  
Resource Allocation Strategy ◽  
Simulation Results ◽  
Leave One Out ◽  
Treatment Prognosis

Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well.

An Algorithm to Separate 1090ES ADS-B Signals

2014 ◽  
Vol 875-877 ◽  
pp. 2158-2163
Author(s):  
Feng Xun Gong ◽  
Kai Wang ◽  
Yan Qiu Ma
Keyword(s):  
Principle Component Analysis ◽  
Carrier Frequency ◽  
Component Analysis ◽  
Projection Algorithm ◽  
Principle Component ◽  
Extended Projection ◽  
Simulation Results ◽  
Strong Ability

Multiple 1090ES ADS-B signals may be overlapped in the receiver systems on the ground. At the same time, 1090ES ADS-B signals may be interfered by A/C replies with the same carrier frequency. To address these problems, firstly, principle component analysis (PCA) is employed to pre-whiten the observed signals in order to reduce the relevance between signals. Then extended projection algorithm (EPA) is applied to separate 1090ES ADS-B signals. Effect of the separated signal is verified by simulation. The performance of this algorithm is also analyzed in several aspects. Simulation results show that this algorithm not only can succeed in separating multiple 1090ES ADS-B signals effectively but also has both higher precision and strong ability of stability.

scholarly journals Predicting Drug-Disease Association Based on Ensemble Strategy

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianlin Wang ◽  
Wenxiu Wang ◽  
Chaokun Yan ◽  
Junwei Luo ◽  
Ge Zhang
Keyword(s):  
Prediction Models ◽  
Drug Repositioning ◽  
Predictive Ability ◽  
Disease Association ◽  
New Drugs ◽  
Similarity Network ◽  
Disease Similarity ◽  
Ensemble Strategy ◽  
Disease Associations ◽  
Reducing Costs

Drug repositioning is used to find new uses for existing drugs, effectively shortening the drug research and development cycle and reducing costs and risks. A new model of drug repositioning based on ensemble learning is proposed. This work develops a novel computational drug repositioning approach called CMAF to discover potential drug-disease associations. First, for new drugs and diseases or unknown drug-disease pairs, based on their known neighbor information, an association probability can be obtained by implementing the weighted K nearest known neighbors (WKNKN) method and improving the drug-disease association information. Then, a new drug similarity network and new disease similarity network can be constructed. Three prediction models are applied and ensembled to enable the final association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. The experimental results demonstrate that the developed approach outperforms recent state-of-the-art prediction models. Case studies further confirm the predictive ability of the proposed method. Our proposed method can effectively improve the prediction results.

Sign in / Sign up

Export Citation Format

Share Document