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Nuclear Norm Clustering: a promising alternative method for clustering tasks.

ABSTRACT: Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nuclear Norm Clustering (NNC, available at https://sourceforge.net/projects/nnc/), an algorithm that can be used in various fields as a promising alternative to the k-means clustering method. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulated annealing techniques to choose an optimal label vector that minimizes nuclear norm of the pooled within cluster residual matrix. To evaluate the performance of the NNC algorithm, we compared the performance of both 15 public datasets and 2 genome-wide association studies (GWAS) on psoriasis, comparing our method with other classic methods. The results indicate that NNC method has a competitive performance in terms of F-score on 15 benchmarked public datasets and 2 psoriasis GWAS datasets. So NNC is a promising alternative method for clustering tasks.

SUBMITTER: Wang Y

PROVIDER: S-EPMC6052164 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Nuclear Norm Clustering: a promising alternative method for clustering tasks.

Wang Yi Y Li Yi Y Qiao Chunhong C Liu Xiaoyu X Hao Meng M Shugart Yin Yao YY Xiong Momiao M Jin Li L

Scientific reports 20180718 1

Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nuclear Norm Clustering (NNC, available at https://sourceforge.net/projects/nnc/), an algorithm that can be used in various fields as a promising alternative to the k-means clustering method. The NNC algorith ...[more]

PMID: 30022093

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Project description:MotivationComputational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug-disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug-disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug-disease associations are highly correlated. In other words, the drug-disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug-disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug-disease associations.ResultsIn this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug-disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug-drug and disease-disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug-disease network, which integrates the drug-drug, drug-disease and disease-disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug-disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR.Availability and implementationThe code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:The Flaviviridae are a family of positive-sense, single-stranded RNA enveloped viruses, and their members belong to a single genus, Flavivirus. Flaviviruses are found in mosquitoes and ticks; they are etiological agents of: dengue fever, Japanese encephalitis, West Nile virus infection, Zika virus infection, tick-borne encephalitis, and yellow fever, among others. Only a few flavivirus vaccines have been licensed for use in humans: yellow fever, dengue fever, Japanese encephalitis, tick-borne encephalitis, and Kyasanur forest disease. However, improvement is necessary in vaccination strategies and in understanding of the immunological mechanisms involved either in the infection or after vaccination. This is especially important in dengue, due to the immunological complexity of its four serotypes, cross-reactive responses, antibody-dependent enhancement, and immunological interference. In this context, mucosal vaccines represent a promising alternative against flaviviruses. Mucosal vaccination has several advantages, as inducing long-term protective immunity in both mucosal and parenteral tissues. It constitutes a friendly route of antigen administration because it is needle-free and allows for a variety of antigen delivery systems. This has promoted the development of several ways to stimulate immunity through the direct administration of antigens (e.g., inactivated virus, attenuated virus, subunits, and DNA), non-replicating vectors (e.g., nanoparticles, liposomes, bacterial ghosts, and defective-replication viral vectors), and replicating vectors (e.g., Salmonella enterica, Lactococcus lactis, Saccharomyces cerevisiae, and viral vectors). Because of these characteristics, mucosal vaccination has been explored for immunoprophylaxis against pathogens that enter the host through mucosae or parenteral areas. It is suitable against flaviviruses because this type of immunization can stimulate the parenteral responses required after bites from flavivirus-infected insects. This review focuses on the advantages of mucosal vaccine candidates against the most relevant flaviviruses in either humans or animals, providing supporting data on the feasibility of this administration route for future clinical trials.

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Nuclear Norm Clustering: a promising alternative method for clustering tasks.

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Nuclear Norm Clustering: a promising alternative method for clustering tasks.

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