NMCMDA: neural multicategory MiRNA–disease association prediction

2021 ◽  
Author(s):  
Jingru Wang ◽  
Jin Li ◽  
Kun Yue ◽  
Li Wang ◽  
Yuyun Ma ◽  
...  
Keyword(s):  
Cost Effective ◽  
Underlying Mechanism ◽  
Disease Association ◽  
Disease Category ◽  
Convolutional Network ◽  
Time Saving ◽  
Disease Associations ◽  
Multiple Category ◽  
Latent Representations ◽  
Main Components

Abstract Motivation There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA–disease associations are time-saving and cost-effective that are highly desired for us. Results We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA–disease association prediction (NMCMDA) for predicting multiple-category miRNA–disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA–disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA–disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA–disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

scholarly journals EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shanchen Pang ◽  
Yu Zhuang ◽  
Xinzeng Wang ◽  
Fuyu Wang ◽  
Sibo Qiao
Keyword(s):  
Information Aggregation ◽  
Disease Association ◽  
Supplementary Information ◽  
Graph Structure ◽  
Association Network ◽  
Convolutional Network ◽  
Edge Information ◽  
Biological Studies ◽  
Convolution Model ◽  
Disease Associations

Abstract Background A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge. Results In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.

scholarly journals EOESGC: Predicting miRNA−disease Associations Based on Embedding of Embedding and Simplified Graph Convolutional Network

2021 ◽  
Author(s):  
Shanchen Pang ◽  
yu Zhuang ◽  
Xinzeng Wang ◽  
Fuyu Wang ◽  
Sibo Qiao
Keyword(s):  
Positional Information ◽  
Disease Association ◽  
Feature Representation ◽  
Experimental Results ◽  
Graph Structure ◽  
Convolutional Network ◽  
Structure Information ◽  
Biological Studies ◽  
Disease Associations ◽  
Computational Difficulty

Abstract Background: A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA−disease associations could provide us a root cause understanding on the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time consuming and costly. Therefore, we come up with more efficient models to solve this challenge. Results: In this work, we propose a deep learning model called EOESGC to predict potential miRNA−disease associations based on embedding of embedding and simplified convolutional network. Firstly, a coupled heterogeneous graph is constructed by using the integrated disease similarity, integrated miRNA similarity and miRNA−disease association networks where parts of the connected edges with less similarity values are removed to simplify the graph structure. The initial feature representation of nodes in the graph is learned using the embedding of embedding model(EOE) based on the principle that the nodes with associations are close to each other and the nodes without association are far from each other. The use of EOE can effectively learn the positional information among nodes and protect the graph structure information to some extent. Then the initial features of the nodes are fed into the simplified graph convolutional network(SGC), and in this step we only use miRNA−disease association network to further simplify the graph structure and thus reduce the computational complexity. Finally, feature embeddings of both miRNA and disease spliced into the MLP for prediction. The two graph simplifications of our model effectively reduce the computational difficulty, and the experimental results show that our model can indeed predict the potential miRNA−disease associations effectively. Compared with the latest published models, our model shows better results. On EOESGC evaluation part, the AUC, AUPR and F1 of our model are 0.9658, 0.8543 and 0.8644 by 5−fold cross validation respectively. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion: The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA−disease associations.

scholarly journals Deep belief network–Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction

2020 ◽  
Vol 16 ◽  
pp. 117693432091970 ◽  
Author(s):  
Yulian Ding ◽  
Fei Wang ◽  
Xiujuan Lei ◽  
Bo Liao ◽  
Fang-Xiang Wu
Keyword(s):  
Matrix Factorization ◽  
Critical Role ◽  
Experimental Studies ◽  
Score Function ◽  
Disease Association ◽  
Deep Belief Network ◽  
Time Saving ◽  
Belief Network ◽  
Related Data ◽  
Disease Associations

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies.

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

2020 ◽  
Vol 36 (8) ◽  
pp. 2538-2546 ◽  
Author(s):  
Jin Li ◽  
Sai Zhang ◽  
Tao Liu ◽  
Chenxi Ning ◽  
Zhuoxuan Zhang ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Matrix Completion ◽  
Disease Association ◽  
Supplementary Information ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Feature Representations ◽  
Disease Similarity ◽  
Disease Associations ◽  
Association Data

Abstract Motivation Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA. Results We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs. Availability and implementation https://github.com/ljatynu/NIMCGCN/ Supplementary information Supplementary data are available at Bioinformatics online.

E-Voting Kepala Desa dan Modal Sosial

2019 ◽  
Vol 2 (6) ◽  
pp. 759
Author(s):  
Khaulah Afifah ◽  
Lala M Kolopaking ◽  
Zessy Ardinal Barlan
Keyword(s):  
Social Capital ◽  
Random Sampling ◽  
Qualitative Data ◽  
Sampling Technique ◽  
Cost Effective ◽  
Simple Random Sampling ◽  
Community Based ◽  
Time Saving ◽  
The Social ◽  
Voting System

Head of a village election with e-voting system is a new thing for community The success level of e-voting system can be reached by fulfil several principles in order to the implementation going effective and the result of the election can be accepted by all. The objectives of this research is to analyze the relation between the success level of e-voting system with social capital of the community. This research is carried out with the quantitative approach and supported by qualitative data. This research takes 60 respondents using simple random sampling technique. The results showed that the success level of e-voting has a correlation with the level of social capital of the community. Based on the field study, the social capital of the community is classified as high. The high social capital makes the implementation of e-voting successful and the success level is also high, because in the election ten years ago occurred a conflict. The community considers e-voting easier and more practical, cost effective and time-saving, and the results of e-voting are also reliable. A practical and fast of e-voting system can be a solution especially for “rural-urban” community who are busy or work outside the village.Keywords: E-voting, the success level of the system, social capital Pemilihan kepala desa dengan sistem e-voting merupakan hal yang baru bagi masyarakat. Keberhasilan penerapan sistem e-voting dilihat dari terpenuhinya beberapa prinsip agar penerapannya berlangsung efektif dan hasilnya dapat diterima oleh seluruh masyarakat. Penelitian ini bertujuan untuk menganalisis hubungan tingkat keberhasilan sistem e-voting dalam pemilihan kepala desa dengan tingkat modal sosial masyarakat. Bentuk penelitian ini adalah penelitian kuantitatif yang didukung oleh analisis data kualitatif. Penelitian ini mengambil enam puluh responden dengan teknik simple random sampling. Hasil penelitian menunjukkan bahwa tingkat keberhasilan e-voting memiliki hubungan dengan tingkat modal sosial masyarakat. Berdasarkan kajian di lapang, modal sosial masyarakat tergolong tinggi. Tingginya modal sosial tersebut membuat pelaksanaan e-voting berhasil dan tingkat keberhasilannya juga tergolong tinggi karena pada pemilihan sepuluh tahun silam sempat terjadi konflik. Masyarakat menganggap sistem evoting lebih mudah dan praktis, hemat dalam segi biaya dan waktu, serta hasil dari pemilihan juga dapat dipertanggungjawabkan. Sistem e-voting yang praktis dan cepat dapat menjadi solusi khususnya bagi masyarakat daerah “desa-kota” yang memiliki kesibukan atau pekerjaan di luar desa.Kata Kunci: E-voting, keberhasilan sistem, modal sosial. 

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.

Kinetic Behaviour of Amylase According to pH: A New Perspective for Starch Hydrolysis Process

2020 ◽  
Vol 16 (2) ◽  
pp. 135-144
Author(s):  
Ravneet K. Grewal ◽  
Baldeep Kaur ◽  
Gagandeep Kaur
Keyword(s):  
Metal Ions ◽  
Competitive Inhibition ◽  
Deae Cellulose ◽  
Kinetic Studies ◽  
Cost Effective ◽  
Starch Hydrolysis ◽  
Divalent Metal Ions ◽  
Activity Data ◽  
Time Saving ◽  
Alkaline Conditions

Background: Amylases are the most widely used biocatalysts in starch saccharification and detergent industries. However, commercially available amylases have few limitations viz. limited activity at low or high pH and Ca2+ dependency. Objective: The quest for exploiting amylase for diverse applications to improve the industrial processes in terms of efficiency and feasibility led us to investigate the kinetics of amylase in the presence of metal ions as a function of pH. Methods: The crude extract from soil fungal isolate cultures is subjected to salt precipitation, dialysis and DEAE cellulose chromatography followed by amylase extraction and is incubated with divalent metal ions (i.e., Ca2+, Fe2+, Cu2+, and Hg2+); Michaelis-Menton constant (Km), and maximum reaction velocity (Vmax) are calculated by plotting the activity data obtained in the absence and presence of ions, as a function of substrate concentration in Lineweaver-Burk Plot. Results: Kinetic studies reveal that amylase is inhibited un-competitively at 5mM Cu2+ at pH 4.5 and 7.5, but non-competitively at pH 9.5. Non-competitive inhibition of amylase catalyzed starch hydrolysis is observed with 5mM Hg2+ at pH 9.5, which changes to mixed inhibition at pH 4.5 and 7.5. At pH 4.5, Ca2+ induces K- and V-type activation of amylase catalyzed starch hydrolysis; however, the enzyme has V-type activation at 7mM Ca2+ under alkaline conditions. Also, K- and V-type of activation of amylase is observed in the presence of 7mM Fe2+ at pH 4.5 and 9.5. Conclusion: These findings suggest that divalent ions modulation of amylase is pH dependent. Furthermore, a time-saving and cost-effective solution is proposed to overcome the challenges of the existing methodology of starch hydrolysis in starch and detergent industries.

A revised FMEA application to the quality control management

2014 ◽  
Vol 31 (7) ◽  
pp. 788-810 ◽  
Author(s):  
Claudia Paciarotti ◽  
Giovanni Mazzuto ◽  
Davide D’Ettorre
Keyword(s):  
Quality Control ◽  
Failure Modes ◽  
Cost Effective ◽  
Weight Functions ◽  
Time Saving ◽  
Content Type ◽  
Complete Procedure ◽  
Medium Enterprise ◽  
Control Management ◽  
Critical Situations

Purpose – The purpose of this paper is to propose a cost-effective, time-saving and easy-to-use failure modes and effects analysis (FMEA) system applied on the quality control of supplied products. The traditional FMEA has been modified and adapted to fit the quality control features and requirements. The paper introduces a new and revised FMEA approach, where the “failure concept” has been modified with “defect concept.” Design/methodology/approach – The typical FMEA parameters have been modified, and a non-linear scale has been introduced to better evaluate the FMEA parameters. In addition, two weight functions have been introduced in the risk priority number (RPN) calculus in order to consider different critical situations previously ignored and the RPN is assigned to several similar products in order to reduce the problem of complexity. Findings – A complete procedure is provided in order to assist managers in deciding on the critical suppliers, the creation of homogeneous families overcome the complexity of single product code approach, in RPN definition the relative importance of factors is evaluated. Originality/value – This different approach facilitates the quality control managers acting as a structured and “friendly” decision support system: the quality control manager can easily evaluate the critical situations and simulate different scenarios of corrective actions in order to choose the best one. This FMEA technique is a dynamic tool and the performed process is an iterative one. The method has been applied in a small medium enterprise producing hydro massage bathtub, shower, spas and that commercializes bathroom furniture. The firm application has been carried out involving a cross-functional and multidisciplinary team.

SGAN4AbSum: A Semantic-Enhanced Generative Adversarial Network for Abstractive Text Summarization

2021 ◽  
Author(s):  
Tham Vo
Keyword(s):  
Ground Truth ◽  
Text Summarization ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Deep Recurrent Neural Network ◽  
Benchmark Datasets ◽  
Latent Representations ◽  
Abstractive Summarization

Abstract In abstractive summarization task, most of proposed models adopt the deep recurrent neural network (RNN)-based encoder-decoder architecture to learn and generate meaningful summary for a given input document. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. Moreover, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also make the generated summaries unnatural and incoherent. To deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model in which train the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network (GCN) textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines.

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