iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning

2020 ◽  
Author(s):  
Hang Wei ◽  
Yong Xu ◽  
Bin Liu
Keyword(s):  
Low Cost ◽  
Principal Component ◽  
Disease Association ◽  
Biological Data ◽  
Sequence Information ◽  
Learning Approach ◽  
Challenging Problem ◽  
Disease Treatment ◽  
Disease Associations ◽  
Key Features

Abstract Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

scholarly journals IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rong Zhu ◽  
Yong Wang ◽  
Jin-Xing Liu ◽  
Ling-Yun Dai
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Cross Validation ◽  
Search Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Biological Data ◽  
Learning Technology ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. Results Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. Conclusions We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

scholarly journals NEMPD: A network embedding-based miRNA-protein-disease network method for the miRNA-disease association prediction

2020 ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Zhan-Heng Chen ◽  
Leon Wong ◽  
Hai-Cheng Yi
Keyword(s):  
Structural Information ◽  
Disease Association ◽  
Human Diseases ◽  
Single Type ◽  
Mirna Sequence ◽  
Sequence Information ◽  
Network Embedding ◽  
Disease Network ◽  
Graph Representations ◽  
Disease Associations

Abstract Background As an important non-coding RNA newly discovered in recent years, MicroRNA (miRNA) plays an important role in a series of life processes and is closely associated with a variety of human diseases. Hence, the identification of potential miRNA-disease associations can make great contributions to the research and treatment of human diseases. However, to our knowledge, many of the existing state-of-the-art computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. Results In this paper, a network embedding-based the tripartite miRNA-protein-disease network (NEMPD) method was proposed for the prediction of miRNA-disease associations. Firstly, a tripartite miRNA-protein-disease network is created by integrating known miRNA-protein and protein-disease associations. Then, we utilize the network representation method-Learning Graph Representations with Global Structural Information (GraRep) to obtain the behavior information (associations with proteins in the network) of miRNAs and diseases. Secondly, the behavior information of miRNAs and diseases is combined with the attribute information of them (disease semantic similarity and miRNA sequence information) to represent miRNA-disease pairs. Thirdly, the prediction model was established based on these known miRNA-disease pairs and the Random Forest algorithm. In the results, under five-fold cross validation, the average prediction accuracy, sensitivity, and AUC of NEMPD is 85.41%, 80.96%, and 91.58%. Furthermore, the performance of NEMPD was also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. Conclusions NEMPD has a good performance in predicting the potential associations between miRNAs and diseases and has great potency in the field of miRNA-disease association prediction in the future.

Hierarchical Extension Based on the Boolean Matrix for LncRNA-Disease Association Prediction

2020 ◽  
Vol 20 (6) ◽  
pp. 452-460
Author(s):  
Lin Tang ◽  
Yu Liang ◽  
Xin Jin ◽  
Lin Liu ◽  
Wei Zhou
Keyword(s):  
Computational Models ◽  
Characteristic Curve ◽  
Experimental Studies ◽  
Potential Method ◽  
Disease Association ◽  
Biological Data ◽  
Boolean Matrix ◽  
Disease Associations ◽  
Association Data ◽  
Association Matrix

Background: Accumulating experimental studies demonstrated that long non-coding RNAs (LncRNAs) play crucial roles in the occurrence and development progress of various complex human diseases. Nonetheless, only a small portion of LncRNA–disease associations have been experimentally verified at present. Automatically predicting LncRNA–disease associations based on computational models can save the huge cost of wet-lab experiments. Methods and Result: To develop effective computational models to integrate various heterogeneous biological data for the identification of potential disease-LncRNA, we propose a hierarchical extension based on the Boolean matrix for LncRNA-disease association prediction model (HEBLDA). HEBLDA discovers the intrinsic hierarchical correlation based on the property of the Boolean matrix from various relational sources. Then, HEBLDA integrates these hierarchical associated matrices by fusion weights. Finally, HEBLDA uses the hierarchical associated matrix to reconstruct the LncRNA– disease association matrix by hierarchical extending. HEBLDA is able to work for potential diseases or LncRNA without known association data. In 5-fold cross-validation experiments, HEBLDA obtained an area under the receiver operating characteristic curve (AUC) of 0.8913, improving previous classical methods. Besides, case studies show that HEBLDA can accurately predict candidate disease for several LncRNAs. Conclusion: Based on its ability to discover the more-richer correlated structure of various data sources, we can anticipate that HEBLDA is a potential method that can obtain more comprehensive association prediction in a broad field.

scholarly journals NEMPD: A Network Embedding-Based miRNA-Protein-Disease Network Method for the miRNA-Disease Association Prediction

2020 ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Zhan-Heng Chen ◽  
Leon Wong ◽  
Hai-Cheng Yi
Keyword(s):  
Structural Information ◽  
Disease Association ◽  
Human Diseases ◽  
Single Type ◽  
Mirna Sequence ◽  
Sequence Information ◽  
Network Embedding ◽  
Disease Network ◽  
Graph Representations ◽  
Disease Associations

Abstract Background: As an important non-coding RNA newly discovered in recent years, MicroRNA (miRNA) plays an important role in a series of life processes and is closely associated with a variety of human diseases. Hence, the identification of potential miRNA-disease associations can make great contributions to the research and treatment of human diseases. However, to our knowledge, many of the existing state-of-the-art computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules.Results: In this paper, a network embedding-based the tripartite miRNA-protein-disease network (NEMPD) method was proposed for the prediction of miRNA-disease associations. Firstly, a tripartite miRNA-protein-disease network is created by integrating known miRNA-protein and protein-disease associations. Then, we utilize the network representation method-Learning Graph Representations with Global Structural Information (GraRep) to obtain the behavior information (associations with proteins in the network) of miRNAs and diseases. Secondly, the behavior information of miRNAs and diseases is combined with the attribute information of them (disease semantic similarity and miRNA sequence information) to represent miRNA-disease pairs. Thirdly, the prediction model was established based on these known miRNA-disease pairs and the Random Forest algorithm. In the results, under five-fold cross validation, the prediction accuracy, sensitivity, and AUC of NEMPD is 85.41%, 80.96%, and 91.58%. Furthermore, the performance of NEMPD was also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases.Conclusions: NEMPD has a good performance in predicting the potential associations between miRNAs and diseases and has great potency in the field of miRNA-disease association prediction in the future.

scholarly journals iMDA-BN: Identification of miRNA-Disease Associations based on the Biological Network and Graph Embedding Algorithm

2020 ◽  
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Domain Knowledge ◽  
Biological Network ◽  
Representation Learning ◽  
Biological Properties ◽  
Disease Association ◽  
Sequence Information ◽  
Network Representation ◽  
Disease Associations

AbstractBenefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.

EHAI: Enhanced Human Microbe-Disease Association Identification

2020 ◽  
Vol 21 (11) ◽  
pp. 1078-1084
Author(s):  
Ruizhi Fan ◽  
Chenhua Dong ◽  
Hu Song ◽  
Yixin Xu ◽  
Linsen Shi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Human Health ◽  
Complex Diseases ◽  
Disease Association ◽  
Computational Results ◽  
Computational Approaches ◽  
Disease Diagnostics ◽  
Disease Associations ◽  
Association Discovery ◽  
Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

scholarly journals Evaluation of indigenous aromatic rice cultivars from sub-Himalayan Terai region of India for nutritional attributes and blast resistance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Debayan Mondal ◽  
Prudveesh Kantamraju ◽  
Susmita Jha ◽  
Gadge Sushant Sundarrao ◽  
Arpan Bhowmik ◽  
...  
Keyword(s):  
Nutritive Value ◽  
Amylose Content ◽  
Blast Resistance ◽  
Starch Content ◽  
Low Cost ◽  
Principal Component ◽  
Blast Disease ◽  
Basmati Rice ◽  
Phenotypic Traits ◽  
Rice Cultivars

AbstractIndigenous folk rice cultivars often possess remarkable but unrevealed potential in terms of nutritional attributes and biotic stress tolerance. The unique cooking qualities and blissful aroma of many of these landraces make it an attractive low-cost alternative to high priced Basmati rice. Sub-Himalayan Terai region is bestowed with great agrobiodiversity in traditional heirloom rice cultivars. In the present study, ninety-nine folk rice cultivars from these regions were collected, purified and characterized for morphological and yield traits. Based on traditional importance and presence of aroma, thirty-five genotypes were selected and analyzed for genetic diversity using micro-satellite marker system. The genotypes were found to be genetically distinct and of high nutritive value. The resistant starch content, amylose content, glycemic index and antioxidant potential of these genotypes represented wide variability and ‘Kataribhog’, ‘Sadanunia’, ‘Chakhao’ etc. were identified as promising genotypes in terms of different nutritional attributes. These cultivars were screened further for resistance against blast disease in field trials and cultivars like ‘Sadanunia’, ‘T4M-3-5’, ‘Chakhao Sampark’ were found to be highly resistant to the blast disease whereas ‘Kalonunia’, ‘Gobindabhog’, ‘Konkanijoha’ were found to be highly susceptible. Principal Component analysis divided the genotypes in distinct groups for nutritional potential and blast tolerance. The resistant and susceptible genotypes were screened for the presence of the blast resistant pi genes and association analysis was performed with disease tolerance. Finally, a logistic model based on phenotypic traits for prediction of the blast susceptibility of the genotypes is proposed with more than 80% accuracy.

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

Correlating Variations in the Dynamic Resistance Signature to Weld Strength in Resistance Spot Welding Using Principal Component Analysis

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
David W. Adams ◽  
Cameron D. E. Summerville ◽  
Brendan M. Voss ◽  
Jack Jeswiet ◽  
Matthew C. Doolan
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Spot Welding ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Weld Strength ◽  
Dynamic Resistance ◽  
Resistance Spot ◽  
Resistance Spot Welds ◽  
Key Features

Traditional quality control of resistance spot welds by analysis of the dynamic resistance signature (DRS) relies on manual feature selection to reduce the dimensionality prior to analysis. Manually selected features of the DRS may contain information that is not directly correlated to strength, reducing the accuracy of any classification performed. In this paper, correlations between the DRS and weld strength are automatically detected by calculating correlation coefficients between weld strength and principal components of the DRS. The key features of the DRS that correlate to weld strength are identified in a systematic manner. Systematically identifying relevant features of the DRS is useful as the correlations between weld strength and DRS may vary with process parameters.

scholarly journals A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. A. Camilleri ◽  
S. B. Eickhoff ◽  
S. Weis ◽  
J. Chen ◽  
J. Amunts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Executive Functioning ◽  
Factor Model ◽  
Five Factor Model ◽  
Principal Component ◽  
Function System ◽  
Learning Approach ◽  
Psychometric Data ◽  
Machine Learning Approach

AbstractWhile a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis–Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.

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