Fast optimization of non-negative matrix tri-factorization

Large annotated cell line collections have been proven to enable the prediction of drug response in the pre-clinical setting. We present an enhancement of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from the Cancer Cell Line Encyclopedia (CCLE), containing the connections among cell lines and drugs by means of their IC50 values, and we integrated it by linking cell lines to their respective tissue of origin and genomic profile. We performed two different kind of experiments: a) prediction of missing values in the matrix, b) prediction of the complete drug profile of a new cell line, demonstrating the validity of the method in both scenarios.

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“…, where L i−1 and L i are respectively the values of the loss function after the last and the previous iterations [13].…”

Section: Methodsmentioning

confidence: 99%

A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity

Testa

Pidò²,

Pinoli³

2022

Lecture Notes in Computer Science

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“…However, our as shown in the previous experiments, our method performs better. Most of this time complexity is due to the multiplicative update rule used in NMF-based algorithms and can be reduced using alternative approaches as discussed in [67]. Lazega Law Firm [68] is a multiplex social network with 71 nodes and three layers representing Co-work, Friendship and Advice relationships between partners and associates of a corporate law firm.…”

Section: ) Scalabilty Analysismentioning

confidence: 99%

Community Detection in Multiplex Networks Based on Orthogonal Nonnegative Matrix Tri-Factorization

Ortiz-Bouza,

Aviyente

2024

IEEE Access

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Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necessitating the use of multiplex networks where multiple links are used to model the interactions. One of the major tools for inferring network topology is community detection. Although there are numerous works on community detection in single-layer networks, existing community detection methods for multiplex networks mostly learn a common community structure across layers and do not take the heterogeneity across layers into account. In this paper, we introduce a new multiplex community detection method that identifies communities that are common across layers as well as those that are unique to each layer. The proposed method, Multiplex Orthogonal Nonnegative Matrix Tri-Factorization, represents the adjacency matrix of each layer as the sum of two low-rank matrix factorizations corresponding to the common and private communities, respectively. Unlike most of the existing methods which require the number of communities to be pre-determined, the proposed method also introduces a two stage method to determine the number of common and private communities. The proposed algorithm is evaluated on synthetic and real multiplex networks, as well as for multiview clustering applications, and compared to state-of-the-art techniques.

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“…In [54] the authors compared different approaches for solving the NMTF problem on six large data-sets. Comparing alternating least squares, projected gradients, and coordinate descent methods they concluded that methods based on projected gradients and coordinate descent converge up to twenty-four times faster than multiplicative update rules and coordinate descent-based NMTF converges up to sixteen times faster compared to well-established methods.…”

Section: Related Workmentioning

confidence: 99%

Four algorithms to solve symmetric multi-type non-negative matrix tri-factorization problem

Hribar¹,

Hrga²,

Papa³

et al. 2020

Preprint

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In this paper, we consider the symmetric multi-type non-negative matrix tri-factorization problem (SNMTF), which attempts to factorize several symmetric non-negative matrices simultaneously. This can be considered as a generalization of the classical non-negative matrix tri-factorization problem and includes a non-convex objective function which is a multivariate sixth degree polynomial and a has convex feasibility set. It has a special importance in data science, since

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Fast optimization of non-negative matrix tri-factorization

Cited by 16 publications

References 47 publications

A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity

A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity

Community Detection in Multiplex Networks Based on Orthogonal Nonnegative Matrix Tri-Factorization

Four algorithms to solve symmetric multi-type non-negative matrix tri-factorization problem

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