Low-rank Matrix Completion Model Research Articles

MDAlmc: A Novel Low-rank Matrix Completion Model for MiRNADisease Association Prediction by Integrating Similarities among MiRNAs and Diseases.

The importance of microRNAs (miRNAs) has been emphasized by an increasing number of studies, and it is well-known that miRNA dysregulation is associated with a variety of complex diseases. Revealing the associations between miRNAs and diseases are essential to disease prevention, diagnosis, and treatment. However, traditional experimental methods in validating the roles of miRNAs in diseases could be very expensive, labor-intensive and time-consuming. Thus, there is a growing interest in predicting miRNA-disease associations by computational methods. Though many computational methods are in this category, their prediction accuracy needs further improvement for downstream experimental validation. In this study, we proposed a novel model to predict miRNA-disease associations by low-rank matrix completion (MDAlmc) integrating miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In the 5-fold cross-validation, MDAlmc achieved an average AUROC of 0.8709 and AUPRC of 0.4172, better than those of previous models. Among the case studies of three important human diseases, the top 50 predicted miRNAs of 96% (breast tumors), 98% (lung tumors), and 90% (ovarian tumors) have been confirmed by previous literatures. And the unconfirmed miRNAs were also validated to be potential disease-associated miRNAs. MDAlmc is a valuable computational resource for miRNA-disease association prediction.

Matrix completion via modified schatten 2/3-norm

Low-rank matrix completion is a hot topic in the field of machine learning. It is widely used in image processing, recommendation systems and subspace clustering. However, the traditional method uses the nuclear norm to approximate the rank function, which leads to only the suboptimal solution. Inspired by the closed-form formulation of L_{2/3} regularization, we propose a new truncated schatten 2/3-norm to approximate the rank function. Our proposed regularizer takes full account of the prior rank information and achieves a more accurate approximation of the rank function. Based on this regularizer, we propose a new low-rank matrix completion model. Meanwhile, a fast and efficient algorithm are designed to solve the proposed model. In addition, a rigorous mathematical analysis of the convergence of the proposed algorithm is provided. Finally, the superiority of our proposed model and method is investigated on synthetic data and recommender system datasets. All results show that our proposed algorithm is able to achieve comparable recovery performance while being faster and more efficient than state-of-the-art methods.

Fast Construction of the Radio Map Based on the Improved Low-Rank Matrix Completion and Recovery Method for an Indoor Positioning System

With the development of information technology, indoor positioning technology has been rapidly evolving. Due to the advantages of high positioning accuracy, low cost, and wide coverage simultaneously, received signal strength- (RSS-) based WLAN indoor positioning technology has become one of the mainstream technologies. A radio map is the basis for the realization of the WLAN positioning system. However, by reasons of the huge workload of RSS collection, the instability of wireless signal strength, and the disappearance of signals caused by the occlusion of people and objects, the construction of a radio map is time-consuming and inefficient. In order to rapidly deploy the WLAN indoor positioning system, an improved low-rank matrix completion method is proposed to construct the radio map. Firstly, we evenly arrange a small number of reference points (RP) in the positioning area and collect RSS data on the RP to construct the radio map. Then, the low-rank matrix completion method is used to fill a small amount of data in the radio map into a complete database. The Frobenius parameter (F-parameter) is introduced into the traditional low-rank matrix completion model to control the instability of the model solution when filling the data. To solve the noise problem caused by environment and equipment, a low-rank matrix recovery algorithm is used to eliminate noise. The experimental results show that the improved algorithm achieves the expected goal.

An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.

LRMCMDA: Predicting miRNA-Disease Association by Integrating Low-Rank Matrix Completion With miRNA and Disease Similarity Information

Identifying disease-related microRNAs (miRNAs) is crucial to understanding the etiology and pathogenesis of many diseases. However, existing computational methods are facing a few dilemmas such as lacking “negative samples” (i.e. confirmed unrelated miRNA-disease pairs). In this study, we proposed LRMCMDA, a low-rank matrix completion-based method to predict miRNA-disease associations. LRMCMDA firstly constructs a bipartite miRNA-disease graph from known associations and defines its R-projected miRNA graph, in which two miRNAs are connected if they are adjacent to the same disease in the bipartite graph. Similarly, we can define its D-projected disease graph. It then infers negative samples by assuming that connecting an unrelated miRNA-disease pair in the bipartite graph will change its R-projected miRNA graph and D-projected disease graph. Providing with both known miRNA-disease associations and negative samples, LRMCMDA infers associations between all miRNAs and diseases using a low-rank matrix completion model, in which miRNA similarity and disease similarity are incorporated into regularization terms. The assumption is that similar miRNAs will associate with similar diseases and vice versa. We compared LRMCMDA with a few state-of-the-art algorithms on several established miRNA-disease databases. LRMCMDA achieves an AUC of 0.8882 on the 5-fold cross-validation, significantly outperforming canonical methods when predicting miRNA-disease associations, and associating miRNAs with isolated diseases. The experimental results demonstrate that LRMCMDA effectively infers novel miRNA-disease associations. In addition, the case studies on cancers have further proven that LRMCMDA is useful in identifying potential cancer-associated miRNAs for experimental validation.

IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data

In recent years, more and more studies have shown that microRNAs (miRNAs) play a key role in many important biological processes. Dysregulation of miRNAs can lead to a variety of diseases like cancers, thus predicting potential miRNA-disease associations is important for understanding drug development and disease pathogenesis, diagnosis and treatment. It is known that experimental methods to validate miRNA-disease associations typically involve miRNA knockout or knockdown, which is time and labor-intensive. As a result, computational models have been developed to predict unknown miRNA-disease associations from available information related to miRNAs, diseases, genes, and so on. However, their performances are yet to be improved. Noticing that appropriately combining multiple data-source is usually helpful for improving prediction accuracy, we have developed IMDAILM: Inferring miRNA-Disease Association by integrating lncRNA and miRNA data, a low-rank matrix completion model integrating miRNA, long noncoding RNA (lncRNA) and disease information to predict miRNA-disease associations. Specifically, the miRNA-disease association network and the lncRNA-disease association network are fused to form a new heterogeneous network consisting of 3 types of nodes representing miRNAs, lncRNAs and diseases. In addition, a negative sample inference method was proposed to infer unrelated miRNA-disease pairs. Based on both heterogeneous network and negative samples, a low-rank matrix completion model is proposed and solved. In practice, IMDAILM achieved an area under the curve (AUC) of 0.8884 for predicting miRNAs associated with diseases under the 5-fold cross-validation (CV), outperforming a few recent methods. IMDAILM also yielded an AUC of 0.8870 for predicting both lncRNAs and miRNAs associated with diseases. In addition, the 5-fold CV results indicate that IMDAILM is also superior to other methods in predicting miRNAs associated with isolated diseases. Finally, we confirmed a few novel predicted miRNAs associated with specific diseases like lung cancers by literature mining. In summary, the integration of lncRNA information into a matrix completion framework contributes to the prediction of miRNA-disease associations.

High Dynamic Range Imaging Based on Bidirectional Structural Similarities and Weighted Low-Rank Matrix Completion

High dynamic range (HDR) imaging, aiming to increase the dynamic range of an image by merging multiexposure images, has attracted much attention. Ghosts are often observed in a resultant image, due to camera motion and object motion in the scene. Low-rank matrix completion (LRMC) provides an effective tool to remove ghosts. However, user specification of the included or excluded regions is required. In this paper, we propose a novel HDR imaging method based on bidirectional structural similarities and weighted low-rank matrix completion. In our method, we first propose the bidirectional structural similarities containing forward-projection structural similarity (FPSS) and backward-projection structural similarity (BPSS) to divide each image into four groups: motion region, saturated region in the source image, saturated region in the reference image, and static and unsaturated regions. Then, the weight maps and the motion maps constructed based on FPSS and BPSS are introduced in the weighted LRMC model to reconstruct the background irradiance maps. Experiments are conducted on several challenging image sets with complex scene, and the results show that the proposed method outperforms three current state-of-the-art methods and Photoshop cs6 and is robust to the reference image.

Exposure Fusion for Dynamic Scenes Combining Retinex Theory and Low-Rank Matrix Completion

Exposure fusion (EF) directly generates a low dynamic range (LDR) image with similar image quality to the high dynamic range (HDR) image by combining images of different exposures to overcome the limited dynamic range of common digital cameras. EF for dynamic scenes is challenging not only due to the difficulty in removing various ghost artifacts but also because of the requirement of preserving details, especially in the saturated regions. Recently, low-rank (LR) matrix completion (LRMC) has been shown to be efficient in separating the latent background from the sparse motion in the irradiance domain. However, the performance of LRMC models strongly relies on the estimated irradiance images. To address this problem, this paper proposes a novel EF method combining Retinex theory and the LRMC model. The proposed method consists of four steps. First, according to Retinex theory, an image is decomposed into illumination and reflection components. Second, the LRMC model is applied to the reflection component to generate the background reflection component and the sparse error. Third, the motion map is modeled as a Markov random field (MRF), integrating the sparse error with the ordering constraint across all the illumination components. Finally, all the illumination components are fused via a pyramid-based method, where the weight maps are defined based on the obtained motion map and illumination. The experimental results show that the proposed method outperforms the state-of-the-art methods particularly in preserving details in saturated regions.

Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity.

The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies.

DTransfer: extremely low cost localization irrelevant to targets and regions for activity recognition

Multiple location-based device-free activity recognition systems indicate that some activities related to specific locations can be inferred from the location system and adding location information can improve the accuracy of activity recognition. Therefore, localization technology is the basis for activity recognition and other applications. Radio-Map is an effective measure in Device-free localization (DFL). Traditional fingerprint systems that can provide such accuracy are suffering from human cost in Radio-Map construction and update. Although the human cost in update phase has been paid attention, the higher costs caused by the initially created are ignored. In addition, existing systems assume that RSS change measurements caused by different targets are fixed distribution in any region. The two drawbacks will greatly affect the practicability and robustness of Radio-Map. In this paper, we propose, DTransfer, an extremely low-cost DFL approach that localize different kinds of targets in different regions. We design an optimized low-rank matrix completion model based on singular value decomposition (SVD) to construct the sensing matrix (i.e., radio-map) of the original region, which greatly reduces the overhead. Next, we employ a rigorously designed quadratic transfer scheme to accurately locate different categories of targets in different regions. Finally, we apply the location information to the activity recognition algorithm; experiments have shown that the accuracy of the algorithm for adding location information is increased by approximately 6%. Extensive experimental results illustrate that DTransfer achieves delightful performance.

Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses.

Low-rank matrix completion has been demonstrated to be powerful in predicting antigenic distances among influenza viruses and vaccines from partially revealed hemagglutination inhibition table. Meanwhile, influenza hemagglutinin (HA) protein sequences are also effective in inferring antigenic distances. Thus, it is natural to integrate HA protein sequence information into low-rank matrix completion model to help infer influenza antigenicity, which is critical to influenza vaccine development. We have proposed a novel algorithm called biological matrix completion with side information (BMCSI), which first measures HA protein sequence similarities among influenza viruses (especially on epitopes) and then integrates the similarity information into a low-rank matrix completion model to predict influenza antigenicity. This algorithm exploits both the correlations among viruses and vaccines in serological tests and the power of HA sequence in predicting influenza antigenicity. We applied this model into H3N2 seasonal influenza virus data. Comparing to previous methods, we significantly reduced the prediction root-mean-square error in a 10-fold cross validation analysis. Based on the cartographies constructed from imputed data, we showed that the antigenic evolution of H3N2 seasonal influenza is generally S-shaped while the genetic evolution is half-circle shaped. We also showed that the Spearman correlation between genetic and antigenic distances (among antigenic clusters) is 0.83, demonstrating a globally high correspondence and some local discrepancies between influenza genetic and antigenic evolution. Finally, we showed that 4.4%±1.2% genetic variance (corresponding to 3.11 ± 1.08 antigenic distances) caused an antigenic drift event for H3N2 influenza viruses historically. The software and data for this study are available at http://bi.sky.zstu.edu.cn/BMCSI/. jialiang.yang@mssm.edu or pinganhe@zstu.edu.cn. Supplementary data are available at Bioinformatics online.