scholarly journals Predicting lincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization

2021 ◽  
Vol 11 ◽  
Author(s):  
Yong Lin ◽  
Xiaoke Ma
Keyword(s):  
Matrix Factorization ◽  
Disease Gene ◽  
State Of The Art ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Ribonucleic Acids ◽  
Genetic Interaction Network ◽  
Gene Associations ◽  
Shed Light ◽  
Non Negative Matrix Factorization

Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is both costly and time-consuming. Therefore, it is necessary to design computational approaches to predict the disease-lincRNA associations that shed light on the mechanisms of diseases. In this study, we develop a co-regularized non-negative matrix factorization (aka Cr-NMF) to identify potential disease-lincRNA associations by integrating the gene expression of lincRNAs, genetic interaction network for mRNA genes, gene-lincRNA associations, and disease-gene associations. The Cr-NMF algorithm factorizes the disease-lincRNA associations, while the other associations/interactions are integrated using regularization. Furthermore, the regularization does not only preserve the topological structure of the lincRNA co-expression network, but also maintains the links “lincRNA → gene → disease.” Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy on predicting the disease-lincRNA associations. The model and algorithm provide an effective way to explore disease-lncRNA associations.

scholarly journals Using a Panchromatic Image to Improve Hyperspectral Unmixing

2020 ◽  
Vol 12 (17) ◽  
pp. 2834
Author(s):  
Simon Rebeyrol ◽  
Yannick Deville ◽  
Véronique Achard ◽  
Xavier Briottet ◽  
Stephane May
Keyword(s):  
Matrix Factorization ◽  
State Of The Art ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Endmember Extraction ◽  
Local Approach ◽  
Hyperspectral Unmixing ◽  
Panchromatic Image ◽  
Art Methods ◽  
Non Negative Matrix Factorization

Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that induce limitations, such as the existence of at least one pure pixel for each material. This work presents a new approach aiming to overcome some of these limitations by introducing a co-registered panchromatic image in the unmixing process. Our method, called Heterogeneity-Based Endmember Extraction coupled with Local Constrained Non-negative Matrix Factorization (HBEE-LCNMF), has several steps: a first set of endmembers is estimated based on a heterogeneity criterion applied on the panchromatic image followed by a spectral clustering. Then, in order to complete this first endmember set, a local approach using a constrained non-negative matrix factorization strategy, is proposed. The performance of our method, in regards of several criteria, is compared to those of state-of-the-art methods obtained on synthetic and satellite data describing urban and periurban scenes, and considering the French HYPXIM/HYPEX2 mission characteristics. The synthetic images are built with real spectral reflectances and do not contain a pure pixel for each endmember. The satellite images are simulated from airborne acquisition with the spatial and spectral features of the mission. Our method demonstrates the benefit of a panchromatic image to reduce some well-known limitations in unmixing hyperspectral data. On synthetic data, our method reduces the spectral angle between the endmembers and the real material spectra by 46% compared to the Vertex Component Analysis (VCA) and N-finder (N-FINDR) methods. On real data, HBEE-LCNMF and other methods yield equivalent performance, but, the proposed method shows more robustness over the data sets compared to the tested state-of-the-art methods. Moreover, HBEE-LCNMF does not require one to know the number of endmembers.

scholarly journals Construction and application of a protein and genetic interaction network (yeast interactome)

2009 ◽  
Vol 37 (7) ◽  
pp. e54-e54 ◽  
Author(s):  
Gregory R. Stuart ◽  
William C. Copeland ◽  
Micheline K. Strand
Keyword(s):  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interaction Network

scholarly journals Network motif analysis of a multi-mode genetic-interaction network

2007 ◽  
Vol 8 (8) ◽  
pp. R160 ◽  
Author(s):  
R James Taylor ◽  
Andrew F Siegel ◽  
Timothy Galitski
Keyword(s):  
Genetic Interaction ◽  
Network Motif ◽  
Interaction Network ◽  
Motif Analysis ◽  
Genetic Interaction Network ◽  
Multi Mode

scholarly journals mSphere of Influence: Comprehensive Genetic Analysis

mSphere ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Jason M. Peters
Keyword(s):  
Antibiotic Resistance ◽  
Genetic Analysis ◽  
Gene Networks ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Cellular Function ◽  
Biofuel Production ◽  
Wiring Diagram ◽  
Genetic Interaction Network ◽  
New Biology

ABSTRACT Jason M. Peters works in the fields of antibiotic resistance and biofuel production. In this mSphere of Influence article, he reflects on how the paper “A global genetic interaction network maps a wiring diagram of cellular function” by Costanzo et al. (Science 353:aaf1420, 2016, https://doi.org/10.1126/science.aaf1420) has impacted his work by highlighting the power of gene networks to uncover new biology.

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