scholarly journals A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square

2018 ◽  
Vol 9 ◽  
Author(s):  
Yan Zhao ◽  
Xing Chen ◽  
Jun Yin
Keyword(s):  
Matrix Factorization ◽  
Disease Association ◽  
Least Square ◽  
Computational Method ◽  
Non Negative Matrix Factorization

scholarly journals miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhen Shen ◽  
You-Hua Zhang ◽  
Kyungsook Han ◽  
Asoke K. Nandi ◽  
Barry Honig ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Noncoding Rna ◽  
Esophageal Neoplasms ◽  
Kidney Neoplasms ◽  
Disease Association ◽  
Computational Method ◽  
Experimental Identification ◽  
Novel Mirna ◽  
Disease Associations ◽  
High Prediction

As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

scholarly journals Mapping Benthic Habitats by Extending Non-Negative Matrix Factorization to Address the Water Column and Seabed Adjacency Effects

2020 ◽  
Vol 12 (13) ◽  
pp. 2072
Author(s):  
Mireille Guillaume ◽  
Audrey Minghelli ◽  
Yannick Deville ◽  
Malik Chami ◽  
Louis Juste ◽  
...  
Keyword(s):  
Optical Properties ◽  
Water Column ◽  
Matrix Factorization ◽  
Coastal Areas ◽  
Least Square ◽  
Local Analysis ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Benthic Habitats ◽  
Non Negative Matrix Factorization

Monitoring of coastal areas by remote sensing is an important issue. The interest of using an unmixing method to determine the seabed composition from hyperspectral aerial images of coastal areas is investigated. Unmixing provides both seabed abundances and endmember reflectances. A sub-surface mixing model is presented, based on a recently proposed oceanic radiative transfer model that accounts for seabed adjacency effects in the water column. Two original non-negative matrix factorization ( N M F )-based unmixing algorithms, referred to as W A D J U M (Water ADJacency UnMixing) and W U M (Water UnMixing, no adjacency effects) are developed, assuming as known the water column bio-optical properties. Simulations show that W A D J U M algorithm achieves performance close to that of the N M F -based unmixing of the seabed without any water column, up to 10 m depth. W U M performance is lower and decreases with the depth. The robustness of the algorithms when using erroneous information about the water column bio-optical properties is evaluated. The results show that the abundance estimation is more reliable using W A D J U M approach. W A D J U M is applied to real data acquired along the French coast; the derived abundance maps of the benthic habitats are discussed and compared to the maps obtained using a fixed spectral library and a least-square ( L S ) estimation of the seabed mixing coefficients. The results show the relevance of the W A D J U M algorithm for the local analysis of the benthic habitats.

scholarly journals Design of various Image Compression Methods in Wireless Sensor Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 2608-2615
Keyword(s):  
Image Compression ◽  
Matrix Factorization ◽  
Least Square ◽  
Wireless Sensor ◽  
Raspberry Pi ◽  
Message Queue ◽  
Compression Algorithms ◽  
Compressed Images ◽  
Non Negative Matrix Factorization

The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are restricted. The quality of the images is deficient, and the contents of the images may vary after decoding when we apply image compression algorithms in WSN. Various compression algorithms are compared in this paper. An Image Compression method based on Restricted Boltzmann Machine (RBM), Auto encoders and Non-negative Matrix Factorization (NMF), Least Square Non-Negative Matrix Factorization (LSNMF), Projective Non-Negative Matrix Factorization (PNMF) network are proposed in this paper. For the WSN, we have used a Message Queue Telemetry Transport (MQTT) protocol. We have used a three Raspberry Pi’s to build a WSN; Publisher, Broker, Subscriber. A Publisher, where it can trigger the camera and captures the images then compress it and send it to another raspberry pi which is a MQTT broker. The PSNR values for those image compression methods were analyzed and compared against each other for images evaluated from the MNIST dataset. Along with the simulation results, all these compression methods are implemented using hardware implementation. Raspberry Pi, a single-board computer with in-built Wi-Fi capabilities, was used in establishing a WSN. Message Queue Telemetry Transport (MQTT) protocol was used for transmitting the compressed images across the WSN, that offers fast and reliable transmission

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