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KBMF specifications


Unique identifier OMICS_13136
Alternative name Kernelized Bayesian Matrix Factorization
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages MATLAB, R
Computer skills Advanced
Stability Stable
Maintained No




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Publications for Kernelized Bayesian Matrix Factorization

KBMF citations


A two tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration

BMC Bioinformatics
PMCID: 5896044
PMID: 29642848
DOI: 10.1186/s12859-018-2123-4

[…] 1 are used to construct the Drug-Drug Relation matrix which can be viewed as a type of kernel matrix. Hence, the proposed method is compatible with existing kernel learning approaches.Methods such as kernelized Bayesian matrix factorization, random walk methods can be effectively applied on bi-partite graphs as a mean of data integration. Multi-modal deep learning can also be applied to heterogene […]


A novel heterogeneous network based method for drug response prediction in cancer cell lines

Sci Rep
PMCID: 5820329
PMID: 29463808
DOI: 10.1038/s41598-018-21622-4

[…] ll line-drug sensitivities using both the cell line’s genomic features and the drug’s chemical structure properties. And Ammad-Ud-Din et al. propose a kernelized Bayesian matrix factorization method (KBMF) to predict drug response by integrating the same dataset of cell line genomic and drug chemical properties. Based on the same principle, Wang et al. propose a kernel function to correlate the he […]


An Ameliorated Prediction of Drug–Target Interactions Based on Multi Scale Discrete Wavelet Transform and Network Features

Int J Mol Sci
PMCID: 5578170
PMID: 28813000
DOI: 10.3390/ijms18081781

[…] Is. The WNN constructed an interaction score profile for a new drug compound using chemical and interaction information about known compounds in the dataset. Another matrix factorization-based method—kernelized Bayesian matrix factorization with twin kernels (KBMF2K) []—was proposed by Gönen, M. The novelty of KBMF2K came from the joint Bayesian formulation of projecting drug compounds and target […]


Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

BMC Cancer
PMCID: 5541434
PMID: 28768489
DOI: 10.1186/s12885-017-3500-5

[…] work where each drug-cell line pair integrated genomic features of cell lines with chemical properties of drugs as predictors []. Ammad-ud-din et al. applied kernelized Bayesian matrix factorization (KBMF) method to predict drug responses in GDSC dataset []. The method utilized genomic and chemical properties in addition to drug target information. Liu et al. used drug similarity network and cell […]


Inferring new indications for approved drugs via random walk on drug disease heterogenous networks

BMC Bioinformatics
PMCID: 5259862
PMID: 28155639
DOI: 10.1186/s12859-016-1336-7

[…] rinciple and predict new drug-target associations by iteratively updates the measure of strength between unlinked drug-target pairs by taking all the paths in the network into account []; KBMF2K uses kernelized bayesian matrix factorization with twin kernels to predict drug-target interactions []; DT-Hybrid extends the NBI algorithm by adding domain knowledge including drug-drug similarity and tar […]


Predicting Anticancer Drug Responses Using a Dual Layer Integrated Cell Line Drug Network Model

PLoS Comput Biol
PMCID: 4587957
PMID: 26418249
DOI: 10.1371/journal.pcbi.1004498

[…] microsatellite instability) with chemical properties of drugs to represent each cell line-drug pair, and used neural network to predict drug response in CGP dataset []. Ammad-ud-din et al. proposed a kernelized Bayesian matrix factorization model to integrated drug property matrix and cell line genomic properties matrix []. This kind of approach could capture the nonlinear relationships between dr […]


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KBMF institution(s)
Sage Bionetworks, Seatle, WA, USA; Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland; Department of Computer Science, University of Helsinki, Helsinki, Finland
KBMF funding source(s)
This work was financially supported by the Integrative Cancer Biology Program of the National Cancer Institute (grant no 1U54CA149237) and the Academy of Finland (grant no 140057 and Finnish Centre of Excellence in Computational Inference Research COIN, grant no 251170).

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