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In polychromatic x-ray imaging for nondestructive testing, material science or medical applications, image quality is usually a problem of detecting sample structure in noisy data. This problem is typically stated this way: As many photons…

Instrumentation and Detectors · Physics 2019-08-27 Maximilian Ullherr , Simon Zabler

Evaluating the treatment effects has become an important topic for many applications. However, most existing literature focuses mainly on the average treatment effects. When the individual effects are heavy-tailed or have outlier values,…

Methodology · Statistics 2023-05-11 Yongchang Su , Xinran Li

Whereas quantum complexity theory has traditionally been concerned with problems arising from classical complexity theory (such as computing boolean functions), it also makes sense to study the complexity of inherently quantum operations…

Quantum Physics · Physics 2021-11-12 Gregory Rosenthal , Henry Yuen

RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively…

Genomics · Quantitative Biology 2012-10-11 Barbara Rakitsch , Christoph Lippert , Hande Topa , Karsten Borgwardt , Antti Honkela , Oliver Stegle

We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the…

Methodology · Statistics 2020-06-17 Sorawit Saengkyongam , Ricardo Silva

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent structural alterations that influence genomic diversity and disease susceptibility. While…

Genomics · Quantitative Biology 2024-11-28 Yuheng Liang , Sedigheh Abedini , Nona Farbehi , Hamid Alinejad-Rokny

The genetic basis of multiple phenotypes such as gene expression, metabolite levels, or imaging features is often investigated by testing a large collection of hypotheses, probing the existence of association between each of the traits and…

Applications · Statistics 2015-04-06 Christine Peterson , Marina Bogomolov , Yoav Benjamini , Chiara Sabatti

Motivated by multi-subject experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a group of related networks, which can be considered as a sample from a population of networks. The proposed…

Applications · Statistics 2020-03-24 Subhadeep Paul , Yuguo Chen

The aetiology of polygenic obesity is multifactorial, which indicates that life-style and environmental factors may influence multiples genes to aggravate this disorder. Several low-risk single nucleotide polymorphisms (SNPs) have been…

Genomics · Quantitative Biology 2018-08-27 Casimiro A. Curbelo Montañez , Paul Fergus , Carl Chalmers , Jade Hind

Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with trait diversity and disease susceptibility, yet the functional properties of many genetic variants and their molecular…

Genomics · Quantitative Biology 2018-03-21 Gary Wilk , Rosemary Braun

One size fits all approaches to medicine have become a thing of the past as the understanding of individual differences grows. The paper introduces a test for the presence of heterogeneity in treatment effects in a clinical trial.…

Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample size n and the number of SNPs p can be very large. Recently,…

Methodology · Statistics 2019-03-05 Bingxin Zhao , Hongtu Zhu

Various statistical methods important for genetic analysis are considered and developed. Namely, we concentrate on the multifactor dimensionality reduction, logic regression, random forests and stochastic gradient boosting. These methods…

Probability · Mathematics 2011-06-29 Alexander Bulinski , Oleg Butkovsky , Alexey Shashkin , Pavel Yaskov

Generalized linear models are often misspecified due to overdispersion, heteroscedasticity and ignored nuisance variables. Existing quasi-likelihood methods for testing in misspecified models often do not provide satisfactory type-I error…

Methodology · Statistics 2020-05-13 Jesse Hemerik , Jelle J Goeman , Livio Finos

Many testing problems are readily amenable to randomised tests such as those employing data splitting. However despite their usefulness in principle, randomised tests have obvious drawbacks. Firstly, two analyses of the same dataset may…

Methodology · Statistics 2024-09-05 F. Richard Guo , Rajen D. Shah

In the search for genetic factors that are associated with complex heritable human traits, considerable attention is now being focused on rare variants that individually have small effects. In response, numerous recent papers have proposed…

Methodology · Statistics 2014-09-10 Andriy Derkach , Jerry F. Lawless , Lei Sun

Statistical inference of heterogeneous treatment effects (HTEs) across predefined subgroups is challenging when units interact because treatment effects may vary by pre-treatment variables, post-treatment exposure variables (that measure…

Econometrics · Economics 2024-10-02 Julius Owusu

NLP-assisted solutions to support qualitative data analysis have gained considerable traction. However, no unified evaluation framework exists which can account for the many different settings in which qualitative researchers may employ…

Computation and Language · Computer Science 2026-04-22 Alvin Po-Chun Chen , Rohan Das , Dananjay Srinivas , Alexandra Barry , Maksim Seniw , Maria Leonor Pacheco

Copy number variants (CNVs) account for more polymorphic base pairs in the human genome than do single nucleotide polymorphisms (SNPs). CNVs encompass genes as well as noncoding DNA, making these polymorphisms good candidates for functional…

Methodology · Statistics 2010-10-26 Sebastian Zöllner , Tanya M. Teslovich

Estimating global pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications. We…

Machine Learning · Computer Science 2020-07-14 Tianyu Cui , Pekka Marttinen , Samuel Kaski