scholarly journals Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wen Dai ◽  
Xi Liu ◽  
Yibo Gao ◽  
Lin Chen ◽  
Jianglong Song ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
New Drugs ◽  
Biological Information ◽  
Gene Interactions ◽  
Computational Techniques ◽  
Interaction Data ◽  
Factorization Model ◽  
The Matrix ◽  
Disease Associations ◽  
Novel Drug

There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space, which includes drug-gene interactions, disease-gene interactions, and gene-gene interactions, is aimed at providing molecular biological information for prediction of drug-disease associations. The rationality lies in our belief that association between drug and disease has its evidence in the interactome network of genes. Experiments show that the integration of genomic space is indeed effective. Drugs, diseases, and genes are described with feature vectors of the same dimension, which are retrieved from the interaction data. Then a matrix factorization model is set up to quantify the association between drugs and diseases. Finally, we use the matrix factorization model to predict novel indications for drugs.

scholarly journals Followee Recommendation in Microblog Using Matrix Factorization Model with Structural Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Yu ◽  
Robin G. Qiu
Keyword(s):  
Social Network ◽  
Matrix Factorization ◽  
Information Sources ◽  
Quality Information ◽  
High Quality ◽  
Structure Information ◽  
Factorization Model ◽  
Proposed Model ◽  
The Matrix ◽  
High Quality Information

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

Attribute interaction aware matrix factorization method for recommendation

2021 ◽  
Vol 25 (5) ◽  
pp. 1115-1130
Author(s):  
Yongquan Wan ◽  
Lihua Zhu ◽  
Cairong Yan ◽  
Bofeng Zhang
Keyword(s):  
Parallel Computing ◽  
Matrix Factorization ◽  
Time Complexity ◽  
Optimization Strategy ◽  
Computing Technology ◽  
Training Time ◽  
Latent Vector ◽  
Factorization Model ◽  
The Matrix ◽  
Original Rating

Matrix factorization (MF) models are effective and easy to expand and are widely used in industry, such as rating prediction and item recommendation. The basic MF model is relatively simple. In practical applications, side information such as attributes or implicit feedback is often combined to improve accuracy by modifying the model and optimizing the algorithm. In this paper, we propose an attribute interaction-aware matrix factorization (AIMF) method for recommendation tasks. We partition the original rating matrix into different sub-matrices according to the attribute interactions, train each sub-matrix independently, and merge all the latent vectors to generate the final score. Since the generated sub-matrices vary in size, an adaptive regularization coefficient optimization strategy and an adaptive latent vector dimension optimization strategy are proposed for sub-matrix training, and a variety of latent vector merging methods are put forward. The method AIMF has two advantages. When the original rating matrix is particularly large, the training time complexity of the MF-based model becomes higher and the update cost of the model is also higher. In AIMF, because each sub-matrix is usually much smaller than the original rating matrix, the training time complexity is greatly reduced after using parallel computing technology. Secondly, in AIMF, it is not necessary to modify the matrix factorization model to incorporate attributes and their interactive information into the model to improve the performance. The experimental results on the two classic public datasets MovieLens 1M and MovieLens 100k show that AIMF can not only effectively improve the accuracy of recommendation, but also make full use of parallel computing technology to improve training efficiency without modifying the matrix factorization model.

scholarly journals TrustRec: An effective approach to exploit implicit trust and distrust relationships along with explicitones for accurate recommendations

2020 ◽  
pp. 39-39
Author(s):  
Reyhani Hamedani ◽  
Irfan Ali ◽  
Jiwon Hong ◽  
Sang-Wook Kim
Keyword(s):  
Matrix Factorization ◽  
Computational Cost ◽  
Effective Solution ◽  
Trust Networks ◽  
Novel Approach ◽  
Factorization Model ◽  
Implicit Trust ◽  
The Matrix ◽  
Effectiveness And Efficiency ◽  
Single Matrix

Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks. In this paper, we point out the problems of existing trust-aware recommendation approaches as follows: (P1) exploiting sparse explicit trust and distrust relationships; (P2) considering a misleading assumption that a user pair having a trust/distrust relationship certainly has a similar/dissimilar preference in practice; (P3) employing the transitivity of distrust relationships. Then, we propose TrustRec, a novel approach based on the matrix factorization that provides an effective solution to each of the afore mentioned problems and incorporates all of them in a single matrix factorization model. Furthermore, TrustRec exploits only top-k most similar trustees and dissimilar distrustees of each user to improve both the computational cost and accuracy. The results of our extensive experiments demonstrate that TructRec outperforms existing approaches in terms of both effectiveness and efficiency.

scholarly journals Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback

2018 ◽  
Author(s):  
Yong Liu ◽  
Lifan Zhao ◽  
Guimei Liu ◽  
Xinyan Lu ◽  
Peng Gao ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Diffusion Rate ◽  
State Of The Art ◽  
Implicit Feedback ◽  
Interaction Data ◽  
Dynamic Matrix ◽  
Factorization Model ◽  
Diffusion Rates ◽  
Item Attributes ◽  
Over Time

Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not valid in most scenarios, where users’ preferences and item attributes heterogeneously drift over time. To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates. More specifically, DBLMF extends logistic matrix factorization to model the probability a user would like to interact with an item at a given timestamp, and a diffusion process to connect latent embeddings over time. In addition, an efficient Bayesian inference algorithm has also been proposed to make DBLMF scalable on large datasets. The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods.

scholarly journals netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis

2019 ◽  
Author(s):  
Rebecca Elyanow ◽  
Bianca Dumitrascu ◽  
Barbara E. Engelhardt ◽  
Benjamin J. Raphael
Keyword(s):  
Single Cell ◽  
Matrix Factorization ◽  
Gene Interactions ◽  
Rna Expression ◽  
Dimensional Representation ◽  
Analysis Methods ◽  
The Matrix ◽  
Low Dimensional ◽  
Cell Expression ◽  
Zero Counts

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) enables high throughput measurement of RNA expression in individual cells. Due to technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard analysis methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells, leveraging the observation that cells generally occupy a small number of RNA expression states.ResultsWe introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc combines network-regularized non-negative matrix factorization with a procedure for handling zero inflation in transcript count matrices. The matrix factorization results in a low-dimensional representation of the transcript count matrix, which imputes gene abundance for both zero and non-zero entries and can be used to cluster cells. The network regularization leverages prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be close in the low-dimensional representation. We show that netNMF-sc outperforms existing methods on simulated and real scRNA-seq data, with increasing advantage at higher dropout rates (e.g. above 60%). Furthermore, we show that the results from netNMF-sc – including estimation of gene-gene covariance – are robust to choice of network, with more representative networks leading to greater performance gains.AvailabilitynetNMF-sc is available at github.com/raphael-group/[email protected]

scholarly journals An Efficient Tensor Completion Method Combining Matrix Factorization and Smoothness

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Leiming Tang ◽  
Xunjie Cao ◽  
Weiyang Chen ◽  
Changbo Ye
Keyword(s):  
Matrix Factorization ◽  
Low Complexity ◽  
Low Rank ◽  
Total Variation Regularization ◽  
Tensor Completion ◽  
Alternating Direction ◽  
Factorization Model ◽  
Efficiency And Effectiveness ◽  
The Matrix ◽  
Accelerate Convergence

In this paper, the low-complexity tensor completion (LTC) scheme is proposed to improve the efficiency of tensor completion. On one hand, the matrix factorization model is established for complexity reduction, which adopts the matrix factorization into the model of low-rank tensor completion. On the other hand, we introduce the smoothness by total variation regularization and framelet regularization to guarantee the completion performance. Accordingly, given the proposed smooth matrix factorization (SMF) model, an alternating direction method of multiple- (ADMM-) based solution is further proposed to realize the efficient and effective tensor completion. Additionally, we employ a novel tensor initialization approach to accelerate convergence speed. Finally, simulation results are presented to confirm the system gain of the proposed LTC scheme in both efficiency and effectiveness.

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

2019 ◽  
Vol 21 (4) ◽  
pp. 1356-1367 ◽  
Author(s):  
Hang Wei ◽  
Bin Liu
Keyword(s):  
Matrix Factorization ◽  
Complex Disease ◽  
False Negative ◽  
Computational Method ◽  
Circular Rnas ◽  
Disease Mechanism ◽  
Disease Similarity ◽  
The Matrix ◽  
Disease Associations ◽  
Non Coding Rnas

Abstract Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

scholarly journals Analisis Perbandingan Model Matrix Factorization dan K-Nearest Neighbor dalam Mesin Rekomendasi Collaborative Berbasis Prediksi Rating

2021 ◽  
Vol 5 (4) ◽  
pp. 506
Author(s):  
Janny Eka Prayogo ◽  
Aries Suharso ◽  
Adhi Rizal
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Absolute Error ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
Factorization Model ◽  
The Matrix

Rating is a form of assessment of the likes or dislikes of a user or customer for an item. Where the higher the rating number given, the item is preferred by customers or users. In the recommendation engine, a set of ratings can be predicted and used as an object to generate a recommendation by the Collaborative Filtering method. In the Collaborative Filtering method, there is a rating prediction model, namely the Matrix Factorization and K-Nearest Neighbor models. This study analyzes the comparison of the two prediction models based on the value of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the prediction results generated using the movielens film rating dataset. From the analysis and testing results, it was found that MAE = 0.6371 and RMSE = 0.8305 for the Matrix Factorization model, while MAE = 0.6742 and RMSE = 0.8863 for the K-Nearest Neighbor model. The best model is Matrix Factorization because the MAE and RMSE values are lower than the K-Nearest Neighbor model and have the closest predicted rating results from the original rating value.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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