Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Higher Order Inference On A Treatment Effect Under Low Regularity Conditions.

ABSTRACT: We describe a novel approach to nonparametric point and interval estimation of a treatment effect in the presence of many continuous confounders. We show the problem can be reduced to that of point and interval estimation of the expected conditional covariance between treatment and response given the confounders. Our estimators are higher order U-statistics. The approach applies equally to the regular case where the expected conditional covariance is root-n estimable and to the irregular case where slower non-parametric rates prevail.

SUBMITTER: Li L

PROVIDER: S-EPMC3088168 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Higher Order Inference On A Treatment Effect Under Low Regularity Conditions.

Li Lingling L Tchetgen Eric Tchetgen ET van der Vaart Aad A Robins James M JM

Statistics & probability letters 20110701 7

We describe a novel approach to nonparametric point and interval estimation of a treatment effect in the presence of many continuous confounders. We show the problem can be reduced to that of point and interval estimation of the expected conditional covariance between treatment and response given the confounders. Our estimators are higher order U-statistics. The approach applies equally to the regular case where the expected conditional covariance is root-n estimable and to the irregular case wh ...[more]

PMID: 21552339

Similar Datasets

Higher-Order Linearization and Regularity in Nonlinear Homogenization.

Project description:We prove large-scale C? regularity for solutions of nonlinear elliptic equations with random coefficients, thereby obtaining a version of the statement of Hilbert's 19th problem in the context of homogenization. The analysis proceeds by iteratively improving three statements together: (i) the regularity of the homogenized Lagrangian L¯ , (ii) the commutation of higher-order linearization and homogenization, and (iii) large-scale C0,1 -type regularity for higher-order linearization errors. We consequently obtain a quantitative estimate on the scaling of linearization errors, a Liouville-type theorem describing the polynomially-growing solutions of the system of higher-order linearized equations, and an explicit (heterogenous analogue of the) Taylor series for an arbitrary solution of the nonlinear equations-with the remainder term optimally controlled. These results give a complete generalization to the nonlinear setting of the large-scale regularity theory in homogenization for linear elliptic equations.

| S-EPMC7217891 | biostudies-literature

Higher-order structure of DNA determines its positioning in cell-size droplets under crowded conditions.

Project description:BackgroundIt is becoming clearer that living cells use water/water (w/w) phase separation to form membraneless organelles that exhibit various important biological functions. Currently, it is believed that the specific localization of biomacromolecules, including DNA, RNA and proteins in w/w microdroplets is closely related to their bio-activity. Despite the importance of this possible role of micro segregation, our understanding of the underlying physico-chemical mechanism is still unrefined. Further research to unveil the underlying mechanism of the localization of macromolecules in relation to their steric conformation in w/w microdroplets is needed.Principal findingsSingle-DNA observation of genome-size DNA (T4 GT7 bacteriophage DNA; 166kbp) by fluorescence microscopy revealed that DNAs are spontaneously incorporated into w/w microdroplets generated in a binary aqueous polymer solution with polyethylene glycol (PEG) and dextran (DEX). Interestingly, DNAs with elongated coil and shrunken conformations exhibit Brownian fluctuation inside the droplet. On the other hand, tightly packed compact globules, as well as assemblies of multiple condensed DNAs, tend to be located near the interface in the droplet.Conclusion and significanceThe specific localization of DNA molecules depending on their higher-order structure occurs in w/w microdroplet phase-separation solution under a binary aqueous polymer solution. Such an aqueous solution with polymers mimics the crowded conditions in living cells, where aqueous macromolecules exist at a level of 30-40 weight %. The specific positioning of DNA depending on its higher-order structure in w/w microdroplets is expected to provide novel insights into the mechanism and function of membraneless organelles and micro-segregated particles in living cells.

| S-EPMC8694483 | biostudies-literature

Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests.

Project description:Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.

| S-EPMC9639209 | biostudies-literature

Hybrid Low-Order and Higher-Order Graph Convolutional Networks.

Project description:With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information. We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models. Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning. The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.

| S-EPMC7336866 | biostudies-literature

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference.

Project description:Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

| S-EPMC10849540 | biostudies-literature

Observation of higher-order non-Hermitian skin effect.

Project description:Beyond the scope of Hermitian physics, non-Hermiticity fundamentally changes the topological band theory, leading to interesting phenomena, e.g., non-Hermitian skin effect, as confirmed in one-dimensional systems. However, in higher dimensions, these effects remain elusive. Here, we demonstrate the spin-polarized, higher-order non-Hermitian skin effect in two-dimensional acoustic higher-order topological insulators. We find that non-Hermiticity drives wave localizations toward opposite edges upon different spin polarizations. More interestingly, for finite systems with both edges and corners, the higher-order non-Hermitian skin effect leads to wave localizations toward two opposite corners for all the bulk, edge and corner states in a spin-dependent manner. We further show that such a skin effect enables rich wave manipulation by configuring the non-Hermiticity. Our study reveals the intriguing interplay between higher-order topology and non-Hermiticity, which is further enriched by the pseudospin degree of freedom, unveiling a horizon in the study of non-Hermitian physics.

| S-EPMC8433224 | biostudies-literature

Order-restricted inference for ordered gene expression (ORIOGEN) data under heteroscedastic variances.

Project description:This article extends the order restricted inference approach for time-course or dose-response gene expression microarray data, introduced by Peddada and colleagues (2003) for the case when gene expression is heteroscedastic over time or dose. The new methodology uses an iterative algorithm to estimate mean expression at various times/doses when mean expression is subject to pre-defined patterns or profiles, known as order-restrictions. Simulation studies reveal that the resulting bootstrap-based methodology for gene selection maintains the false positive rate at the nominal level while competing well with ORIOGEN in terms of power. The proposed methodology is illustrated using a breast cancer cell-line data analyzed by Peddada and colleagues (2003).

| S-EPMC1896056 | biostudies-literature

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction.

Project description:Recently, machine learning methods, including reservoir computing (RC), have been tremendously successful in predicting complex dynamics in many fields. However, a present challenge lies in pushing for the limit of prediction accuracy while maintaining the low complexity of the model. Here, we design a data-driven, model-free framework named higher-order Granger reservoir computing (HoGRC), which owns two major missions: The first is to infer the higher-order structures incorporating the idea of Granger causality with the RC, and, simultaneously, the second is to realize multi-step prediction by feeding the time series and the inferred higher-order information into HoGRC. We demonstrate the efficacy and robustness of the HoGRC using several representative systems, including the classical chaotic systems, the network dynamical systems, and the UK power grid system. In the era of machine learning and complex systems, we anticipate a broad application of the HoGRC framework in structure inference and dynamics prediction.

| S-EPMC10954644 | biostudies-literature

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.

Project description:Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a 'pseudotime' where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.

| S-EPMC5117567 | biostudies-literature

Higher-order quantum spin Hall effect in a photonic crystal.

Project description:The quantum spin Hall effect lays the foundation for the topologically protected manipulation of waves, but is restricted to one-dimensional-lower boundaries of systems and hence limits the diversity and integration of topological photonic devices. Recently, the conventional bulk-boundary correspondence of band topology has been extended to higher-order cases that enable explorations of topological states with codimensions larger than one such as hinge and corner states. Here, we demonstrate a higher-order quantum spin Hall effect in a two-dimensional photonic crystal. Owing to the non-trivial higher-order topology and the pseudospin-pseudospin coupling, we observe a directional localization of photons at corners with opposite pseudospin polarizations through pseudospin-momentum-locked edge waves, resembling the quantum spin Hall effect in a higher-order manner. Our work inspires an unprecedented route to transport and trap spinful waves, supporting potential applications in topological photonic devices such as spinful topological lasers and chiral quantum emitters.

| S-EPMC7387344 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data