Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.

ABSTRACT: The do-calculus is a well-known deductive system for deriving connections between interventional and observed distributions, and has been proven complete for a number of important identifiability problems in causal inference [1, 8, 18]. Nevertheless, as it is currently defined, the do-calculus is inapplicable to causal problems that involve complex nested counterfactuals which cannot be expressed in terms of the "do" operator. Such problems include analyses of path-specific effects and dynamic treatment regimes. In this paper we present the potential outcome calculus (po-calculus), a natural generalization of do-calculus for arbitrary potential outcomes. We thereby provide a bridge between identification approaches which have their origins in artificial intelligence and statistics, respectively. We use po-calculus to give a complete identification algorithm for conditional path-specific effects with applications to problems in mediation analysis and algorithmic fairness.

SUBMITTER: Malinsky D

PROVIDER: S-EPMC6935349 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.

Malinsky Daniel D Shpitser Ilya I Richardson Thomas T

Proceedings of machine learning research 20190401

The do-calculus is a well-known deductive system for deriving connections between interventional and observed distributions, and has been proven complete for a number of important identifiability problems in causal inference [1, 8, 18]. Nevertheless, as it is currently defined, the do-calculus is inapplicable to causal problems that involve complex nested counterfactuals which cannot be expressed in terms of the "do" operator. Such problems include analyses of path-specific effects and dynamic t ...[more]

PMID: 31886462

Similar Datasets

A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology.

Project description:BackgroundBiological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways.ResultsWe propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials. CONCLUSION: The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.

| S-EPMC7414699 | biostudies-literature

Causal effects in microbiomes using interventional calculus.

Project description:Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or "disease" variable, and then computing the causal network, referred to as a "disease network", with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

| S-EPMC7970971 | biostudies-literature

Heritage-specific oral microbiota in Indigenous Australian dental calculus.

Project description:Background and objectivesAboriginal Australians and Torres Strait Islanders (hereafter respectfully referred to as Indigenous Australians) experience a high burden of chronic non-communicable diseases (NCDs). Increased NCD risk is linked to oral diseases mediated by the oral microbiota, a microbial community influenced by both vertical transmission and lifestyle factors. As an initial step towards understanding the oral microbiota as a factor in Indigenous health, we present the first investigation of oral microbiota in Indigenous Australian adults.MethodologyDental calculus samples from Indigenous Australians with periodontal disease (PD; n = 13) and non-Indigenous individuals both with (n = 19) and without PD (n = 20) were characterized using 16S ribosomal RNA gene amplicon sequencing. Alpha and beta diversity, differentially abundant microbial taxa and taxa unique to different participant groups were analysed using QIIME2.ResultsSamples from Indigenous Australians were more phylogenetically diverse (Kruskal-Wallis H = 19.86, P = 8.3 × 10-6), differed significantly in composition from non-Indigenous samples (PERMANOVA pseudo-F = 10.42, P = 0.001) and contained a relatively high proportion of unique taxa not previously reported in the human oral microbiota (e.g. Endomicrobia). These patterns were robust to stratification by PD status. Oral microbiota diversity and composition also differed between Indigenous individuals living in different geographic regions.Conclusions and implicationsIndigenous Australians may harbour unique oral microbiota shaped by their long relationships with Country (ancestral homelands). Our findings have implications for understanding the origins of oral and systemic NCDs and for the inclusion of Indigenous peoples in microbiota research, highlighting the microbiota as a novel field of enquiry to improve Indigenous health.

| S-EPMC9400808 | biostudies-literature

Conditional KIAA1549:BRAF mice reveal brain region- and cell type-specific effects.

Project description:Low-grade brain tumors (pilocytic astrocytomas) that result from a genomic rearrangement in which the BRAF kinase domain is fused to the amino terminal of the KIAA1549 gene (KIAA1549:BRAF fusion; f-BRAF) commonly arise in the cerebellum of young children. To model this temporal and spatial specificity in mice, we generated conditional KIAA1549:BRAF strains that coexpresses green fluorescent protein (GFP). Although both primary astrocytes and neural stem cells (NSCs) from these mice express f-BRAF and GFP as well as exhibit increased MEK activity, only f-BRAF-expressing NSCs exhibit increased proliferation in vitro. Using Cre driver lines in which KIAA1549:BRAF expression was directed to NSCs (f-BRAF; BLBP-Cre mice), astrocytes (f-BRAF; GFAP-Cre mice), and NG2 progenitor cells (f-BRAF; NG2-Cre mice), increased glial cell numbers were observed only in the cerebellum of f-BRAF; BLBP-Cre mice in vivo. The availability of this unique KIAA1549:BRAF conditional transgenic mouse strain will enable future mechanistic studies aimed at defining the developmentally-regulated temporal and spatial determinants that underlie low-grade astrocytoma formation in children.

| S-EPMC3808469 | biostudies-literature

A potential outcomes approach to defining and estimating gestational age-specific exposure effects during pregnancy.

Project description:Many studies seek to evaluate the effects of potentially harmful pregnancy exposures during specific gestational periods. We consider an observational pregnancy cohort where pregnant individuals can initiate medication usage or become exposed to a drug at various times during their pregnancy. An important statistical challenge involves how to define and estimate exposure effects when pregnancy loss or delivery can occur over time. Without proper consideration, the results of standard analysis may be vulnerable to selection bias, immortal time-bias, and time-dependent confounding. In this study, we apply the "target trials" framework of Hernán and Robins in order to define effects based on the counterfactual approach often used in causal inference. This effect is defined relative to a hypothetical randomized trial of timed pregnancy exposures where delivery may precede and thus potentially interrupt exposure initiation. We describe specific implementations of inverse probability weighting, G-computation, and Targeted Maximum Likelihood Estimation to estimate the effects of interest. We demonstrate the performance of all estimators using simulated data and show that a standard implementation of inverse probability weighting is biased. We then apply our proposed methods to a pharmacoepidemiology study to evaluate the potentially time-dependent effect of exposure to inhaled corticosteroids on birthweight in pregnant people with mild asthma.

| S-EPMC8829732 | biostudies-literature

PATH-SCAN: a reporting tool for identifying clinically actionable variants.

Project description:The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.

| S-EPMC4008882 | biostudies-literature

Do-calculus enables estimation of causal effects in partially observed biomolecular pathways.

Project description:MotivationEstimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways.ResultsIn this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl's do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation.Availability and implementationThe code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9235495 | biostudies-literature

Xtalk: a path-based approach for identifying crosstalk between signaling pathways.

Project description:MotivationCells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics.ResultsWe present Xtalk, a path-based approach for identifying pairs of pathways that may crosstalk. Xtalk computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, Xtalk reports the precise interactions and mechanisms that support the identified crosstalk. We applied Xtalk to signaling pathways in the KEGG and NCI-PID databases. We manually curated a gold standard set of 132 crosstalking pathway pairs and a set of 140 pairs that did not crosstalk, for which Xtalk achieved an area under the receiver operator characteristic curve of 0.65, a 12% improvement over the closest competing approach. The area under the receiver operator characteristic curve varied with the pathway, suggesting that crosstalk should be evaluated on a pathway-by-pathway level. We also analyzed an extended set of 658 pathway pairs in KEGG and to a set of more than 7000 pathway pairs in NCI-PID. For the top-ranking pairs, we found substantial support in the literature (81% for KEGG and 78% for NCI-PID). We provide examples of networks computed by Xtalk that accurately recovered known mechanisms of crosstalk.Availability and implementationThe XTALK software is available at http://bioinformatics.cs.vt.edu/~murali/software. Crosstalk networks are available at http://graphspace.org/graphs?tags=2015-bioinformatics-xtalk.Contactategge@vt.edu, murali@cs.vt.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4907392 | biostudies-literature

ON TESTING CONDITIONAL QUALITATIVE TREATMENT EFFECTS.

Project description:Precision medicine is an emerging medical paradigm that focuses on finding the most effective treatment strategy tailored for individual patients. In the literature, most of the existing works focused on estimating the optimal treatment regime. However, there has been less attention devoted to hypothesis testing regarding the optimal treatment regime. In this paper, we first introduce the notion of conditional qualitative treatment effects (CQTE) of a set of variables given another set of variables and provide a class of equivalent representations for the null hypothesis of no CQTE. The proposed definition of CQTE does not assume any parametric form for the optimal treatment rule and plays an important role for assessing the incremental value of a set of new variables in optimal treatment decision making conditional on an existing set of prescriptive variables. We then propose novel testing procedures for no CQTE based on kernel estimation of the conditional contrast functions. We show that our test statistics have asymptotically correct size and non-negligible power against some nonstandard local alternatives. The empirical performance of the proposed tests are evaluated by simulations and an application to an AIDS data set.

| S-EPMC6561732 | biostudies-other

Tumor-specific lytic path "hyperploid progression mediated death": Resolving side effects through targeting retinoblastoma or p53 mutant.

Project description:A major advance was made to reduce the side effects of cancer therapy via the elucidation of the tumor-specific lytic path "hyperploid progression-mediated death" targeting retinoblastoma (Rb) or p53-mutants defective in G1 DNA damage checkpoint. The genetic basis of human cancers was uncovered through the cloning of the tumor suppressor Rb gene. It encodes a nuclear DNA-binding protein whose self-interaction is regulated by cyclin-dependent kinases. A 3D-structure of Rb dimer is shown, confirming its multimeric status. Rb assumes a central role in cell cycle regulation and the "Rb pathway" is universally inactivated in human cancers. Hyperploidy refers to a state in which cells contain one or more extra chromosomes. Hyperploid progression occurs due to continued cell-cycling without cytokinesis in G1 checkpoint-defective cancer cells. The evidence for the triggering of hyperploid progression-mediated death in RB-mutant human retinoblastoma cells is shown. Hence, the very genetic mutation that predisposes to cancer can be exploited to induce lethality. The discovery helped to establish the principle of targeted cytotoxic cancer therapy at the mechanistic level. By triggering the lytic path, targeted therapy with tumor specificity at the genetic level can be developed. It sets the stage for systematically eliminating side effects for cytotoxic cancer therapy.

| S-EPMC7701912 | 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