Results 61 to 70 of about 14,760 (255)

A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering

Molecules, 2017
Detecting genomes with similar expression patterns using clustering techniques plays an important role in gene expression data analysis. Non-negative matrix factorization (NMF) is an effective method for clustering the analysis of gene expression data ...
Rong Zhu, Jin-Xing Liu, Yuan-Ke Zhang, Ying Guo   +3 more
doaj   +1 more source

Infinite Non-Negative Matrix Factorization

, 2010
Publication in the conference proceedings of EUSIPCO, Aalborg, Denmark ...
Mørup, Morten, Schmidt, Mikkel
openaire   +1 more source

LDAcoop: Integrating non‐linear population dynamics into the analysis of clonogenic growth in vitro

Molecular Oncology, EarlyView.
Limiting dilution assays (LDAs) quantify clonogenic growth by seeding serial dilutions of cells and scoring wells for colony formation. The fraction of negative wells is plotted against cells seeded and analyzed using the non‐linear modeling of LDAcoop.
Nikko Brix, Daniel Samaga, Katharina Gehr, Benedek Dankó, Mohamed Schumann, Guido Drexler, Ahmed Alnatsha, Georg Beyer, Ujjwal Mahajan, Martin Selmansberger, Julia Mayerle, Claus Belka, Horst Zitzelsberger, Kirsten Lauber   +13 more
wiley   +1 more source

Semantic Non-Negative Matrix Factorization for Term Extraction

Big Data and Cognitive Computing
This study introduces an unsupervised term extraction approach that combines non-negative matrix factorization (NMF) with word embeddings. Inspired by a pioneering semantic NMF method that employs regularization to jointly optimize document–word and word–
Aliya Nugumanova, Almas Alzhanov, Aiganym Mansurova, Kamilla Rakhymbek, Yerzhan Baiburin   +4 more
doaj   +1 more source

Non Negative Matrix Factorization Clustering Capabilities; Application on Multivariate Image Segmentation [PDF]

Journal of Electrical and Electronics Engineering, 2009
The clustering capabilities of the Non Negative MatrixFactorization algorithm is studied. The basis images are consideredlike the data membership degree to a particular class.A hard clustering algorithm is easily derived based on theseimages.
Cosmin Lazar, Danielle Nuzillard, Patrice Billaudel, Sorin Curila   +3 more
doaj  

Hessian Regularization Based Factorization Algorithm Combining Multi-view and Non-negative Matrix [PDF]

Jisuanji gongcheng, 2017
Non-negative matrix does not consider the manifold of data when represents multi-view data,which results in the ineffective express of the data internal expression.In this paper,Hessian regularized Non-negative Matrix Factorization(NMF) is proposed.By ...
WANG Chaofeng,SHI Jun,WU Jinjie,ZHU Jie
doaj   +1 more source

Multifrontal Non-negative Matrix Factorization

, 2020
Non-negative matrix factorization (Nmf) is an important tool in high-performance large scale data analytics with applications ranging from community detection, recommender system, feature detection and linear and non-linear unmixing. While traditional Nmf works well when the data set is relatively dense, however, it may not extract sufficient structure
Piyush Sao, Ramakrishnan Kannan
openaire   +2 more sources

Plecstatin inhibits hepatocellular carcinoma tumorigenesis and invasion through cytolinker plectin

Molecular Oncology, EarlyView.
The ruthenium‐based metallodrug plecstatin exerts its anticancer effect in hepatocellular carcinoma (HCC) primarily through selective targeting of plectin. By disrupting plectin‐mediated cytoskeletal organization, plecstatin inhibits anchorage‐dependent growth, cell polarization, and tumor cell dissemination.
Zuzana Outla, Jan Kosla, Magdalena Prechova, Lukas Frick, Patricia Bortel, Yasmin Borutzki, Andrea Bileck, Christopher Gerner, Samuel M. Meier‐Menches, Selma Osmanagic‐Myers, Martin Gregor   +10 more
wiley   +1 more source

Recurrent cancer‐associated ERBB4 mutations are transforming and confer resistance to targeted therapies

Molecular Oncology, EarlyView.
We show that the majority of the 18 analyzed recurrent cancer‐associated ERBB4 mutations are transforming. The most potent mutations are activating, co‐operate with other ERBB receptors, and are sensitive to pan‐ERBB inhibitors. Activating ERBB4 mutations also promote therapy resistance in EGFR‐mutant lung cancer.
Veera K. Ojala, Sini Ahonen, Sara Peltola, Aura Tuohisto‐Kokko, Olaya Esparta, Peppi Suominen, Anne Jokilammi, Iman Farahani, Deepankar Chakroborty, Nikol Dibus, Steffen Boettcher, Tomi T. Airenne, Mark S. Johnson, Lisa D. Eli, Klaus Elenius, Kari J. Kurppa   +15 more
wiley   +1 more source

Blind source separation with optimal transport non-negative matrix factorization

EURASIP Journal on Advances in Signal Processing, 2018
Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF ...
Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada   +3 more
doaj   +1 more source