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Heterogeneous, interconnected, systems-level, molecular data have become increasingly available and key in precision medicine. We need to utilize them to better stratify patients into risk groups, discover new biomarkers and targets,…

Other Quantitative Biology · Quantitative Biology 2024-05-17 Natasa Przulj , Noel Malod-Dognin

The major sources of abundant data are constantly expanding with the available data collection methodologies in various applications - medical, insurance, scientific, bio-informatics and business. These data sets may be distributed…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-06-24 Aruna Govada , Sanjay K. Sahay

Microbial communities are widely studied using high-throughput sequencing techniques, such as 16S rRNA gene sequencing. These techniques have attracted biologists as they offer powerful tools to explore microbial communities and investigate…

Applications · Statistics 2021-04-06 Elham Bayat Mokhtari , Benjamin Ridenhour

We present a novel coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant…

Biological Physics · Physics 2009-11-06 G. Getz , E. Levine , E. Domany

Complex systems are fascinating because their rich macroscopic properties emerge from the interaction of many simple parts. Understanding the building principles of these emergent phenomena in nature requires assessing natural complex…

Neurons and Cognition · Quantitative Biology 2022-11-17 Anna Levina , Viola Priesemann , Johannes Zierenberg

One important problem in microbiome analysis is to identify the bacterial taxa that are associated with a response, where the microbiome data are summarized as the composition of the bacterial taxa at different taxonomic levels. This paper…

Applications · Statistics 2016-03-04 Pixu Shi , Anru Zhang , Hongzhe Li

DNA-based biodiversity surveys involve collecting physical samples from survey sites and assaying the contents in the laboratory to detect species via their diagnostic DNA sequences. DNA-based surveys are increasingly being adopted for…

Identifying which taxa in our microbiota are associated with traits of interest is important for advancing science and health. However, the identification is challenging because the measured vector of taxa counts (by amplicon sequencing) is…

Genomics · Quantitative Biology 2020-03-31 Barak Brill , Amnon Amir , Ruth Heller

Joint analysis of multi-omic single-cell data across cohorts has significantly enhanced the comprehensive analysis of cellular processes. However, most of the existing approaches for this purpose require access to samples with complete…

Machine Learning · Computer Science 2024-05-21 Marianne Arriola , Weishen Pan , Manqi Zhou , Qiannan Zhang , Chang Su , Fei Wang

With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for each microbiome sample. One goal of…

Applications · Statistics 2013-05-24 Jun Chen , Hongzhe Li

Envelope model also known as multivariate regression model was proposed to solve the multiple response regression problems. It measures the linear association between predictors and multiple responses by using the minimal reducing subspace…

Methodology · Statistics 2018-05-07 Bochao Jia

The problem of overdispersion in multivariate count data is a challenging issue. Nowadays, it covers a central role mainly due to the relevance of modern technologies data, such as Next Generation Sequencing and textual data from the web or…

Methodology · Statistics 2025-02-24 Noemi Corsini , Cinzia Viroli

The standard methods for detecting differential gene expression are mostly designed for analyzing a single gene expression experiment. When data from multiple related gene expression studies are available, separately analyzing each study is…

Methodology · Statistics 2013-11-07 Yingying Wei , Hongkai Ji

Scientific investigations that incorporate next generation sequencing involve analyses of high-dimensional data where the need to organize, collate and interpret the outcomes are pressingly important. Currently, data can be collected at the…

Machine Learning · Statistics 2016-08-01 Stephen T Rush , Christine H Lee , Washington Mio , Peter T Kim

Many data-science applications involve detecting a shared signal between two high-dimensional variables. Using random matrix theory methods, we determine when such signal can be detected and reconstructed from sample correlations, despite…

Disordered Systems and Neural Networks · Physics 2026-04-07 Arabind Swain , Sean Alexander Ridout , Ilya Nemenman

Factor analysis is a critical component of high dimensional biological data analysis. However, modern biological data contain two key features that irrevocably corrupt existing methods. First, these data, which include longitudinal,…

Methodology · Statistics 2020-09-24 Chris McKennan

Community effects on the behaviour of individuals, the community itself and other communities can be observed in a wide range of applications. This is true in scientific research, where communities of researchers have increasingly to…

Social and Information Networks · Computer Science 2010-12-01 Václav Belák , Marcel Karnstedt , Conor Hayes

Microbiome `omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with the identification of specific bacteria taxa associated with diseases, recent scientific advancements…

Applications · Statistics 2019-10-07 Shuang Jiang , Guanghua Xiao , Andrew Y. Koh , Qiwei Li , Xiaowei Zhan

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is possible to follow the relative abundance of microbes in a community over time. These…

Quantitative Methods · Quantitative Biology 2015-06-18 Charles K. Fisher , Pankaj Mehta

This paper tackles the problem of detecting abrupt changes in the mean of a heteroscedastic signal by model selection, without knowledge on the variations of the noise. A new family of change-point detection procedures is proposed, showing…

Methodology · Statistics 2011-02-01 Sylvain Arlot , Alain Celisse