scholarly journals A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Yan Yu ◽  
Yijie Ding ◽  
Jijun Tang ◽  
...  
Keyword(s):  
Side Effects ◽  
Matrix Factorization ◽  
Side Effect ◽  
Multiple Kernel Learning ◽  
Factorization Method ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Original Matrix ◽  
Multiple Kernel ◽  
Benchmark Datasets

All drugs usually have side effects, which endanger the health of patients. To identify potential side effects of drugs, biological and pharmacological experiments are done but are expensive and time-consuming. So, computation-based methods have been developed to accurately and quickly predict side effects. To predict potential associations between drugs and side effects, we propose a novel method called the Triple Matrix Factorization- (TMF-) based model. TMF is built by the biprojection matrix and latent feature of kernels, which is based on Low Rank Approximation (LRA). LRA could construct a lower rank matrix to approximate the original matrix, which not only retains the characteristics of the original matrix but also reduces the storage space and computational complexity of the data. To fuse multivariate information, multiple kernel matrices are constructed and integrated via Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL) in drug and side effect space, respectively. Compared with other methods, our model achieves better performance on three benchmark datasets. The values of the Area Under the Precision-Recall curve (AUPR) are 0.677, 0.685, and 0.680 on three datasets, respectively.

scholarly journals Multi-Nyström Method Based on Multiple Kernel Learning for Large Scale Imbalanced Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ling Wang ◽  
Hongqiao Wang ◽  
Guangyuan Fu
Keyword(s):  
Kernel Methods ◽  
Large Scale ◽  
Multiple Kernel Learning ◽  
Nonlinear Problems ◽  
Low Rank ◽  
Kernel Learning ◽  
Low Rank Approximation ◽  
Nyström Method ◽  
Nystrom Method ◽  
Multiple Kernel

Extensions of kernel methods for the class imbalance problems have been extensively studied. Although they work well in coping with nonlinear problems, the high computation and memory costs severely limit their application to real-world imbalanced tasks. The Nyström method is an effective technique to scale kernel methods. However, the standard Nyström method needs to sample a sufficiently large number of landmark points to ensure an accurate approximation, which seriously affects its efficiency. In this study, we propose a multi-Nyström method based on mixtures of Nyström approximations to avoid the explosion of subkernel matrix, whereas the optimization to mixture weights is embedded into the model training process by multiple kernel learning (MKL) algorithms to yield more accurate low-rank approximation. Moreover, we select subsets of landmark points according to the imbalance distribution to reduce the model’s sensitivity to skewness. We also provide a kernel stability analysis of our method and show that the model solution error is bounded by weighted approximate errors, which can help us improve the learning process. Extensive experiments on several large scale datasets show that our method can achieve a higher classification accuracy and a dramatical speedup of MKL algorithms.

scholarly journals Predicting the side effects of drugs using matrix factorization on spontaneous reporting database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kohei Fukuto ◽  
Tatsuya Takagi ◽  
Yu-Shi Tian
Keyword(s):  
Side Effects ◽  
Matrix Factorization ◽  
Side Effect ◽  
Cold Start ◽  
Factorization Method ◽  
Mapping Method ◽  
Spontaneous Reports ◽  
The Matrix ◽  
History Of ◽  
Cold Start Problem

AbstractThe severe side effects of some drugs can threaten the lives of patients and financially jeopardize pharmaceutical companies. Computational methods utilizing chemical, biological, and phenotypic features have been used to address this problem by predicting the side effects. Among these methods, the matrix factorization method, which utilizes the side-effect history of different drugs, has yielded promising results. However, approaches that encapsulate all the characteristics of side-effect prediction have not been investigated to date. To address this gap, we applied the logistic matrix factorization algorithm to a database of spontaneous reports to construct a prediction with higher accuracy. We expressed the distinction in the importance of drug-side effect pairs by a weighting strategy and addressed the cold-start problem via an attribute-to-feature mapping method. Consequently, our proposed model improved the prediction accuracy by 2.5% and efficiently handled the cold-start problem. The proposed methodology is expected to benefit applications such as warning systems in clinical settings.

A PSO Based Approach for Producing Optimized Latent Factor in Special Reference to Big Data

2016 ◽  
Vol 7 (3) ◽  
pp. 55-70 ◽  
Author(s):  
Bharat Singh ◽  
Om Prakash Vyas
Keyword(s):  
Big Data ◽  
Matrix Factorization ◽  
Nonnegative Matrix ◽  
Low Rank ◽  
Data Matrix ◽  
Experimental Result ◽  
Low Rank Approximation ◽  
Latent Factor ◽  
New Approach ◽  
Rank Approximation

Now a day's application deal with Big Data has tremendously been used in the popular areas. To tackle with such kind of data various approaches have been developed by researchers in the last few decades. A recent investigated techniques to factored the data matrix through a known latent factor in a lower size space is the so called matrix factorization. In addition, one of the problems with the NMF approaches, its randomized valued could not provide absolute optimization in limited iteration, but having local optimization. Due to this, the authors have proposed a new approach that considers the initial values of the decomposition to tackle the issues of computationally expensive. They have devised an algorithm for initializing the values of the decomposed matrix based on the PSO. In this paper, the auhtors have intended a genetic algorithm based technique while incorporating the nonnegative matrix factorization. Through the experimental result, they will show the proposed method converse very fast in comparison to other low rank approximation like simple NMF multiplicative, and ACLS technique.

A hybrid algorithm for low-rank approximation of nonnegative matrix factorization

2019 ◽  
Vol 364 ◽  
pp. 129-137
Author(s):  
Peitao Wang ◽  
Zhaoshui He ◽  
Kan Xie ◽  
Junbin Gao ◽  
Michael Antolovich ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Hybrid Algorithm ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Rank Approximation

scholarly journals Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Wenjia Niu ◽  
Kewen Xia ◽  
Baokai Zu ◽  
Jianchuan Bai
Keyword(s):  
Recognition Accuracy ◽  
Multiple Kernel Learning ◽  
Low Rank ◽  
Kernel Learning ◽  
Support Vector ◽  
Distribution Characteristics ◽  
Kernel Weights ◽  
Multiple Kernel ◽  
Kernel Approximation ◽  
Low Rank Representation

Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR). It is well-acknowledged that LRR can reduce dimension while retaining the data features under a global low-rank constraint. Furthermore, we redesign the binary-class MKL as the multiclass MKL based on pairwise strategy. Finally, the recognition effect and efficiency of LR-MKL are verified on the datasets Yale, ORL, LSVT, and Digit. Experimental results show that the proposed LR-MKL algorithm is an efficient kernel weights allocation method in MKL and boosts the performance of MKL largely.

scholarly journals Low-Rank Matrix Factorization Method for Multiscale Simulations: A Review

2021 ◽  
Vol 2 ◽  
pp. 286-301
Author(s):  
Mengmeng Li ◽  
Dazhi Ding ◽  
Alexander Heldring ◽  
Jun Hu ◽  
Rushan Chen ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Factorization Method ◽  
Low Rank ◽  
Multiscale Simulations ◽  
Rank Matrix ◽  
Low Rank Matrix

scholarly journals A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Zhikui Chen ◽  
Shan Jin ◽  
Runze Liu ◽  
Jianing Zhang
Keyword(s):  
Big Data ◽  
Matrix Factorization ◽  
Pattern Mining ◽  
Representation Learning ◽  
Factorization Method ◽  
Data Representation ◽  
Deep Architecture ◽  
Factorization Model ◽  
Symmetric Loss ◽  
Benchmark Datasets

Nowadays, deep representations have been attracting much attention owing to the great performance in various tasks. However, the interpretability of deep representations poses a vast challenge on real-world applications. To alleviate the challenge, a deep matrix factorization method with non-negative constraints is proposed to learn deep part-based representations of interpretability for big data in this paper. Specifically, a deep architecture with a supervisor network suppressing noise in data and a student network learning deep representations of interpretability is designed, which is an end-to-end framework for pattern mining. Furthermore, to train the deep matrix factorization architecture, an interpretability loss is defined, including a symmetric loss, an apposition loss, and a non-negative constraint loss, which can ensure the knowledge transfer from the supervisor network to the student network, enhancing the robustness of deep representations. Finally, extensive experimental results on two benchmark datasets demonstrate the superiority of the deep matrix factorization method.

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