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Search · four archives
19 papers · ranked by Valyu relevance
Daniel O’Malley, Velimir V. Vesselinov, Boian S. Alexandrov, Ludmil B. Alexandrov + 1 more
'Ludmil B. Alexandrov' 'Ivan Olier'] D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been…
Renichiro Haba, Masayuki Ohzeki, Kazuyuki Tanaka
factorization Authors: ['Renichiro Haba' 'Masayuki Ohzeki' 'Kazuyuki Tanaka'] Quantum annealing has garnered significant attention as meta-heuristics inspired by quantum physics for combinatorial optimization problems. Among its many applications, nonnegative/binary matrix factorization stands out for its complexity…
Yukino Terui, Yuka Inoue, Yohei Hamakawa, Kosuke Tatsumura + 1 more
'Kazue Kudo'] Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix factorization techniques such as nonnegative matrix factorization (NMF) are often employed.…
Alberto Lumbreras, Louis Filstroff, Cédric Févotte
Binary data matrices can represent many types of data such as social networks, votes, or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this…
Hui Yu, Kui-Tao Mao, Jian-Yu Shi, Hua Huang + 3 more
'Siu-Ming Yiu'] Background Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to…
Duc P. Truong, Erik Skau, Derek DeSantis, Boian S. Alexandrov
A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with the constraint over an auxiliary matrix whose Boolean structure is identical to the initial Boolean data. Then the solution of the nonnegative…
Paul Magron, Cédric Févotte
This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary data and enables interpretability of the factors. We factorize the Bernoulli parameter and consider an…
Yukino Terui, Yukari Inoue, Yohei Hamakawa, Kosuke Tatsumura + 1 more
'Kazue Kudo'] Collaborative filtering generates recommendations based on user-item similarities through rating data, which may involve numerous unrated items. To predict scores for unrated items, matrix factorization techniques, such as nonnegative matrix factorization (NMF), are often employed to predict scores for…
Osman Asif Malik, Hayato Ushijima-Mwesigwa, Arnab Roy, Avradip Mandal + 2 more
Many fundamental problems in data mining can be reduced to one or more NP-hard combinatorial optimization problems. Recent advances in novel technologies such as quantum and quantum-inspired hardware promise a substantial speedup for solving these problems compared to when using general purpose computers but often…
Xihui Lin, Paul C. Boutros
Nonnegative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence, and cannot handle missing values. In addition…
Andrej Čopar, Blaž Zupan, Marinka Zitnik, Holger Fröhlich
Non-negative matrix tri-factorization (NMTF) is a popular technique for learning low-dimensional feature representation of relational data. Currently, NMTF learns a representation of a dataset through an optimization procedure that typically uses multiplicative update rules. This procedure has had limited success, and…
Elina Tjioe, Michael W Berry, Ramin Homayouni
Background Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases…
Genevieve L. Stein-O’Brien, Raman Arora, Aedin C. Culhane, Alexander V. Favorov + 7 more
High-dimensional data is currently standard for biological inquiry. Biological systems are comprised of interrelated gene regulatory mechanisms, gene-gene interactions, and cellular interactions. These interactions induce low-dimensional structure within the high-dimensional data. Matrix factorization, also known as…
Sunho Park, Nabhonil Kar, Jae-Ho Cheong, Tae Hyun Hwang
Accurate identification of pathways associated with cancer phenotypes (e.g., cancer sub-types and treatment outcome) could lead to discovering reliable prognostic and/or predictive biomarkers for better patients stratification and treatment guidance. In our previous work, we have shown that non-negative matrix…
Cesar A. López, Velimir V. Vesselinov, Sandrasegaram Gnanakaran, Boian S. Alexandrov
Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that are capable to decompose and categorize the evolution of thousands if…
Richard Kueng, Joel A. Tropp
This paper studies the problem of decomposing a low-rank matrix into a factor with binary entries, either from {±1} or from {0,1}, and an unconstrained factor. The research answers fundamental questions about the existence and uniqueness of these decompositions. It also leads to tractable factorization algorithms that…
Diba Behnoudfar, Cory Simon, Joshua Schrier
Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas…
Aleksandar Poleksic
Past and current research in systems biology has taken for granted the Euclidean geometry of biological space. This has not only drawn parallels to other fields but has also been convenient due to the ample statistical and numerical optimization tools available to address the core task and downstream machine learning…
Arni Sturluson, Ali Raza, Grant D. McConachie, Daniel Siderius + 2 more
Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A…