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The non-negative matrix factorization toolbox for biological data mining

Yifeng Li, Alioune Ngom
2013 Source Code for Biology and Medicine  
Non-negative matrix factorization (NMF) has been introduced as an important method for mining biological data.  ...  Results: We provide a convenient MATLAB toolbox containing both the implementations of various NMF techniques and a variety of NMF-based data mining approaches for analyzing biological data.  ...  Background Non-negative matrix factorization (NMF) is a matrix decomposition approach which decomposes a nonnegative matrix into two low-rank non-negative matrices [1] .  ... 
doi:10.1186/1751-0473-8-10 pmid:23591137 pmcid:PMC3736608 fatcat:5ziwxpu3e5fsvlorkaju7as7du

bioNMF: a versatile tool for non-negative matrix factorization in biology

Alberto Pascual-Montano, Pedro Carmona-Saez, Monica Chagoyen, Francisco Tirado, Jose M Carazo, Roberto D Pascual-Marqui
2006 BMC Bioinformatics  
In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about  ...  the complex latent relationships in experimental data sets.  ...  the Spanish CSIC and the Canadian NRC (CSIC-050402040003).  ... 
doi:10.1186/1471-2105-7-366 pmid:16875499 pmcid:PMC1550731 fatcat:5tyxoqbuezbfjbgaq6dg6xjvgy

Calypso: a user-friendly web-server for mining and visualizing microbiome–environment interactions

Martha Zakrzewski, Carla Proietti, Jonathan J. Ellis, Shihab Hasan, Marie-Jo Brion, Bernard Berger, Lutz Krause
2016 Bioinformatics  
The software is programmed in Java, PERL and R and the source code is available from Zenodo ( The software is freely available for non-commercial users.  ...  Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets.  ...  Acknowledgements We acknowledge the National eResearch Collaboration Tools and Resources (Nectar) project for providing cloud computing resources.  ... 
doi:10.1093/bioinformatics/btw725 pmid:28025202 pmcid:PMC5408814 fatcat:7bvoabailbca5o7uwgnizmupom

Non-negative Tensor Factorization Based on Alternating Large-scale Non-negativity-constrained Least Squares

Hyunsoo Kim, Haesun Park, Lars Elden
2007 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering  
Non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) have attracted much attention and have been successfully applied to numerous data analysis problems where the components  ...  of the data are necessarily non-negative such as chemical concentrations in experimental results or pixels in digital images.  ...  Rasmus Bro for helpful comments on non-negativity on the fluorescence data set. This work is supported by the National Science Foundation Grants ACI-0305543 and CCF-0621889.  ... 
doi:10.1109/bibe.2007.4375705 dblp:conf/bibe/KimPE07 fatcat:pmg7m3r6hjfu7kid6k52kcxrzu

Versatile Sparse Matrix Factorization and Its Applications in High-Dimensional Biological Data Analysis [chapter]

Yifeng Li, Alioune Ngom
2013 Lecture Notes in Computer Science  
Non-negative matrix factorization and sparse representation models have been successfully applied in high-throughput biological data analysis.  ...  In this paper, we propose our versatile sparse matrix factorization (VSMF) model for biological data mining. We show that many well-known sparse models are specific cases of VSMF.  ...  The training data D must be non-negative for the standard NMF and sparse NMF. For sparse NMF, α and λ are two non-negative parameters.  ... 
doi:10.1007/978-3-642-39159-0_9 fatcat:y4fiywofp5dg5n6xtvg7fzmhqq

BIDEAL: A Toolbox for Bicluster Analysis – Generation, Visualization and Validation [article]

Nishchal K. Verma, T. Sharma, S. Dixit, P. Agrawal, S. Sengupta, V. Singh
2020 arXiv   pre-print
The toolbox can analyze several types of data, including biological data through a graphical user interface.  ...  A single toolbox comprising various biclustering algorithms play a vital role to extract meaningful patterns from the data for detecting diseases, biomarkers, gene-drug association, etc.  ...  INTRODUCTION B ICLUSTERING has become prevalent and useful data mining technique among researchers for analyzing the data.  ... 
arXiv:2007.13737v1 fatcat:5g73flwisradxjhja4ci7sjs2q

A Data Mining Approach for the Prediction of Hepatitis C Virus protease Cleavage Sites

Ahmed mohamed
2011 International Journal of Advanced Computer Science and Applications  
We collected latest accurate data sets to build the prediction model. Also we collected another dataset for the model testing.  ...  There are several factors that can affect the overall prediction accuracy.  ...  Several data mining techniques have been used in solving and analyzing several biological problems.  ... 
doi:10.14569/ijacsa.2011.021227 fatcat:w5kysjmaprg2vbhqkbczwbq5c4

Unexpected functional similarities between gatekeeper tumour suppressor genes and proto-oncogenes revealed by systems biology

Yongzhong Zhao, Richard J Epstein
2011 Journal of Human Genetics  
We performed multidimensional data analysis, non-negative matrix factorization and logistic regression to compare the features of GKs with those of their putative antagonists, the proto-oncogenes (POs)  ...  If correct, this interpretation would explain the hitherto unexplained phenomenon of frequent wild-type GK (for example, p53) overexpression in tumors.  ...  RJE designed the experiments, wrote the paper, and finalized the paper.  ... 
doi:10.1038/jhg.2011.21 pmid:21368766 fatcat:7w6kw4yhufd4fn26hswxb22ifm

TensorSplat: Spotting Latent Anomalies in Time

Danai Koutra, Evangelos E. Papalexakis, Christos Faloutsos
2012 2012 16th Panhellenic Conference on Informatics  
Such multiaspect data, including time-evolving graphs, can be successfully modelled using Tensors.  ...  Our method TENSORSPLAT, at the heart of which lies the "PARAFAC" decomposition method, can give good insights about the large networks that are of interest nowadays, and contributes to spotting micro-clusters  ...  We chose to obtain this bilinear decomposition through the SVD, but in fact, there exist numerous different approaches, e.g. the Non-Negative Matrix Factorization [20] .  ... 
doi:10.1109/pci.2012.60 dblp:conf/pci/KoutraPF12 fatcat:ykpncrpf6fhkrlby5dfrp5xpfy

CBP-JMF: An Improved Joint Matrix Tri-Factorization Method for Characterizing Complex Biological Processes of Diseases

Bingbo Wang, Xiujuan Ma, Minghui Xie, Yue Wu, Yajun Wang, Ran Duan, Chenxing Zhang, Liang Yu, Xingli Guo, Lin Gao
2021 Frontiers in Genetics  
Differently from existing methods, CBP-JMF is based on a joint non-negative matrix tri-factorization framework and is implemented in Python.  ...  Multi-omics molecules regulate complex biological processes (CBPs), which reflect the activities of various molecules in living organisms.  ...  Basically, each non-negative matrix X (p) ∈ R m×n , p = 1, 2, ..., P is factorized into three non-negative matrix factors based on matrix tri-factorization: X (p) ≈ U (p) S (p) V, where molecular coefficient  ... 
doi:10.3389/fgene.2021.665416 pmid:33968140 pmcid:PMC8103031 fatcat:rzwddiqsovfctca7vyobilggya

Partial least squares regression for graph mining

Hiroto Saigo, Nicole Krämer, Koji Tsuda
2008 Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08  
Our method, graph PLS, is efficient and easy to implement, because the weight vector is updated with elementary matrix calculations.  ...  To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm.  ...  Acknowledgements The authors would like to thank Pierre Mahé for data preparation, Ichigaku Takigawa for figure preparation, and Sebastian Nowozin for preparation of MATLAB toolbox and proof reading.  ... 
doi:10.1145/1401890.1401961 dblp:conf/kdd/SaigoKT08 fatcat:ck2djp3slfb3nlsuyqsfsqwj4q

Protein-protein interaction prediction via Collective Matrix Factorization

Qian Xu, Evan Wei Xiang, Qiang Yang
2010 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
Noticing that a network structure can be modeled using a matrix model, in this paper, we introduce the wellknown Collective Matrix Factorization (CMF) technique to 'transfer' usable linkage knowledge from  ...  The network structures of many such networks are sparse, incomplete and noisy, containing many false positive and false negatives.  ...  The target data set Helicobacter pylori dataset consists of 1,458 positives (interacting pairs) and 1,458 negatives (non-interacting pairs).  ... 
doi:10.1109/bibm.2010.5706537 dblp:conf/bibm/XuXY10 fatcat:gxfiq6gngvfedglc65p6au6e2e

Topic Modeling: A Comprehensive Review

Pooja Kherwa, Poonam Bansal
2018 EAI Endorsed Transactions on Scalable Information Systems  
Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents.  ...  Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling.  ...  S. (1999) Learning the parts of objects by non-negative matrix factorization.  ... 
doi:10.4108/eai.13-7-2018.159623 fatcat:lu6al57vp5aahbytyejhqrlzry

Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns

Jinyu Chen, Shihua Zhang
2018 Frontiers in Genetics  
To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization  ...  The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools  ...  ACKNOWLEDGMENTS We appreciate Prof Xianghong Jasmine Zhou at University of California, Los Angeles for her generous help on the MIA package.  ... 
doi:10.3389/fgene.2018.00194 pmid:29910825 pmcid:PMC5992392 fatcat:h54jec3fofeh7fjxfzp6e6qz5u

A primer to frequent itemset mining for bioinformatics

S. Naulaerts, P. Meysman, W. Bittremieux, T. N. Vu, W. Vanden Berghe, B. Goethals, K. Laukens
2013 Briefings in Bioinformatics  
We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.  ...  Owing to these and other interesting properties, these techniques have proven their value in biological data analysis.  ...  FIM toolbox for binary databases GNU GPL 97 KNIME Desktop Data analytics platform GNU GPL 98 MIME Interactive FIM toolbox Research only  ... 
doi:10.1093/bib/bbt074 pmid:24162173 pmcid:PMC4364064 fatcat:khstm5as2jah5fhsskvjrivoku
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