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Search · four archives
13 papers · ranked by Valyu relevance
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…
Stuti Jain, Emilie Chouzenoux, Kriti Kumar, Angshul Majumdar
Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a…
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…
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…
Ko Abe, Shintaro Yuki, Teppei Shimamura
Combinatorial indexing-based single-cell RNA sequencing methods such as sci-RNA-seq and sci-RNA-seq3 now enable the profiling of millions of cells, producing expression matrices that are both extremely sparse and high-dimensional. Conventional nonnegative matrix factorization (NMF) provides an interpretable framework…
Simone Marini, Francesca Vitali, Sara Rampazzi, Andrea Demartini + 1 more
Protein cleavage is an important cellular event, involved in a myriad of processes, from apoptosis to immune response. Bioinformatics provides in silico tools, such as machine learning-based models, to guide target discovery. State-of-the-art models have a scope limited to specific protease families (such as Caspases)…
Lingxi Chen, Jiao Xu, Shuai Cheng Li
With recent advances in high-throughput technologies, matrix factorization techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering insights in the underlying biological processes. Nevertheless, current matrix…
Denis Kleverov, Ekaterina Aladyeva, Alexey Serdyukov, Maxim N. Artyomov
Non-negative matrix factorization (NMF) is one of the most powerful linear algebra tools, which has found application in various areas of data analysis, including computational biology. Despite numerous optimization methods devised for NMF, our comprehension of the inherent topological structure within factorizable…
David Hirst, Morgane Térézol, Laura Cantini, Paul Villoutreix + 2 more
Joint matrix factorization is a popular method for extracting lower dimensional representations of multi-omics data. It disentangles underlying mixtures of biological signals, facilitating efficient sample clustering, disease subtyping, or biomarker identification, for instance. However, when a multi-omics dataset is…
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…
Ragunathan Mariappan, Aishwarya Jayagopal, Ho Zong Sien, Vaibhav Rajan
In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary collections of matrices. The latent factors learnt are rich integrative…
Kensuke Tanioka, Yuki Furotani, Satoru Hiwa
Low-rank approximation is used for interpreting the features of a correlation matrix using visualization tools; however, a low-rank approximation may result in estimation that is far from zero even if the corresponding original value is zero. In such a case, the results lead to a misinterpretation. To overcome this, we…
Evan D. Gorman, Manuel E. Lladser
Ultrametric matrices have a rich structure that is not apparent from their definition. Notably, the subclass of strictly ultrametric matrices are covariance matrices of certain weighted rooted binary trees. In applications, these matrices can be large and dense, making them difficult to store and handle. In this…