Evaluating Directed Acyclic Graphs with DAGMetrics: Insights from Tuber and Soil Microbiome Data

Background/Aim: Identifying confounders based on causal directed acyclic graphs (DAGs) is usually based solely on expert knowledge, although epidemiological studies provide observational data. We applied data-based structure learning algorithms for the specific purpose of identifying confounders, investigating the research question whether particulate matter with an aerodynamic diameter ≤2.5 µg/m3 (PM2.5) affects progression of atherosclerosis, measured as yearly change in carotid intima media thickness (ΔcIMT). Methods: We used 5-year follow-up data of the population-based Heinz Nixdorf Recall Study. Due to computational limitations, potentially relevant variables were restricted to continuously measured data and hence included age, BMI, smoking, blood pressure, HbA1c, LDL/HDL, education, income, neighborhood unemployment (nSES) rate, physical activity and alcohol consumption. Prior knowledge was based on causal chain assumptions regarding major causes of ischaemic heart disease1, and translated to prohibited arcs, i.e. given conditions (e.g. age) are unaffected by lifestyle variables (e.g. smoking) and lifestyle variables are unaffected by subclinical medical determinants (e.g. laboratory measures). We applied a revised structure learning algorithm to learn a causal DAG, including the score-based hill-climbing algorithm, a bootstrap-check to validate the conditional independence structure between variables, and a clustering of minimal sufficient adjustment set (MSAS)-equivalent subsets within the Markov equivalence-class. Results: Based on 2279 participants, the revised structure learning resulted in one MSAS-equivalent cluster, including one DAG of 59 unambiguous arcs. The only biasing path from PM2.5 to ΔcIMT emanated from nSES. Suggested adjustment consisted of four confounder-equivalent MSAS ({nSES}; {age, physical activity, blood pressure}; {age, alcohol consumption, BMI, physical activity, smoking}; {age, BMI, education, income, physical activity, smoking}), yielding an identical exposure effect estimate of 0.002 mm (95%-CI: -0.002, 0.007) increase in cIMT per year per 5 µg/m3 increase in PM2.5. Conclusions: Data-based structure learning returned one unambiguous DAG widely matching general believes. Identical exposure effect estimates of confounder-equivalent MSAS underline a stable (causal) structure.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

The Kullback–Leibler (KL) divergence is a fundamental measure of information geometry that is used in a variety of contexts in artificial intelligence. We show that, when system dynamics are given by distributed nonlinear systems, this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. More specifically, these measures are applicable when selecting a candidate model for a distributed system, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables; however, we can exploit the properties of certain dynamical systems to formulate exact methods based on differential topology. We approach the problem by using reconstruction theorems to derive an analytical expression for the KL divergence of a candidate DAG from the observed dataset. Using this result, we present a scoring function based on transfer entropy to be used as a subroutine in a structure learning algorithm. We then demonstrate its use in recovering the structure of coupled Lorenz and Rössler systems.

The K2 algorithm is widely recognised as an effective approach for Bayesian network structure learning, but its performance is highly sensitive to the chosen node order. To mitigate this limitation, we introduce a new method for deriving candidate parents that serve as input to the K2 algorithm. Our approach begins by estimating the Markov blanket of each variable using an elastic net–penalised Markov blanket procedure. We then employ a dependency-based scoring function to identify candidate parents within each Markov blanket. These selected parents are subsequently provided to the K2 algorithm to learn the network structure. Experimental results across multiple datasets show that the proposed method reduces unnecessary edges and achieves substantially better performance than existing approaches.

Covariance estimation and selection for high-dimensional multivariate datasets is a fundamental problem in modern statistics. Gaussian directed acyclic graph (DAG) models are a popular class of models used for this purpose. Gaussian DAG models introduce sparsity in the Cholesky factor of the inverse covariance matrix, and the sparsity pattern in turn corresponds to specific conditional independence assumptions on the underlying variables. A variety of priors have been developed in recent years for Bayesian inference in DAG models, yet crucial convergence and sparsity selection properties for these models have not been thoroughly investigated. Most of these priors are adaptations/generalizations of the Wishart distribution in the DAG context. In this paper, we consider a flexible and general class of these “DAG-Wishart” priors with multiple shape parameters. Under mild regularity assumptions, we establish strong graph selection consistency and establish posterior convergence rates for estimation when the number of variables $p$ is allowed to grow at an appropriate subexponential rate with the sample size $n$.

Reduction of the error rate of melanoma diagnosis, a critical and very dangerous skin cancer that could be treated when early detected, is of major importance. Towards this direction, the present paper presents a novel ensemble classification technique, combining traditional Random Forests with the `Markov Blanket' notion. The proposed algorithm performs an inherent feature selection phase where only truly informative features are carried forward, thus alleviating the curse of dimensionality and augmenting classification performance. It has been evaluated in a high-dimensional and imbalanced dataset of 1041 skin lesion images, which been preprocessed using the ABCD-rule of dermatology. The proposed ensemble classification technique exhibited a higher classification performance in comparison with the classical Random Forest algorithms, as well as other widely-used classification algorithms where standard feature reduction techniques, such as PCA and SVD, have been applied.

An empirical comparison of popular structure learning algorithms with a view to gene network inference

Reducing the complexity of high-dimensional environmental data: An analytical framework using LASSO with considerations of confounding for statistical inference.

Learning the causal structures in high-dimensional datasets allows deriving advanced insights from observational data, thus creating the potential for new applications. One crucial limitation of state-of-the-art methods for learning causal relationships, such as the PC algorithm, is their long execution time. While, in the worst case, the execution time is exponential to the dimension of a given dataset, it is polynomial if the underlying causal structures are sparse. To address the long execution time, parallelized extensions of the algorithm have been developed addressing the Central Processing Unit (CPU) as the primary execution device. While modern multicore CPUs expose a decent level of parallelism, coprocessors, such as Graphics Processing Units (GPUs), are specifically designed to process thousands of data points in parallel, providing superior parallel processing capabilities compared to CPUs.In our work, we leverage the parallel processing power of GPUs to address the drawback of the long execution time of the PC algorithm and develop an efficient GPU-accelerated implementation for Gaussian distribution models. Based on an experimental evaluation of various high-dimensional real-world gene expression datasets, we show that our GPU-accelerated implementation outperforms existing CPU-based versions, by factors up to 700.

1,6-Naphthodipyrrolidone-based donor–acceptor polymers with low bandgap

Rapid Inference of Direct Interactions in Large-Scale Ecological Networks from Heterogeneous Microbial Sequencing Data.

New metal complexes of NNO tridentate ligands: Effect of metal center and co-ligand on biological activity

Transcription of yeast tRNA genes in vitro requires, in addition to RNA polymerase III, two accessory factors which are resolved by ion-exchange chromatography. One of these transcription factors (factor C) binds to tRNA genes. The stability of factor C-tRNA gene complexes is gene-dependent: the tRNAAGGArg gene forms a highly stable complex while tRNA3Leu and tRNATyr gene complexes are unstable under our standard assay conditions. To determine how differences in tRNA gene structure affect factor C binding, mutant tRNATyr genes, internally deleted tRNA3Leu genes and hybrid transcription units containing both tRNATyr and tRNA3Leu segments were compared in their abilities to stably bind factor C. Sequence changes in either of the two highly conserved promoter elements (A block and B block) affect factor C complex stability. Changes towards the consensus sequence increase complex stability while changes away from the consensus sequence drastically reduce stability. Also, the distance separating the A and B blocks affects complex stability; 34-53 bp gives highest stability. These results indicate that the stable binding of transcription factor C to tRNA genes involves interactions with both A block and B block sequences.

Polyol-mineral interactions in the diet of the rat with special reference to the stabilities of polyol-metal complexes

H3N3 avian influenza viruses (AIVs) are less prevalent in poultry than H3N8 viruses. However, although relatively rare, reassortant H3N3 viruses have been known to appear in both domestic poultry and wild birds. In this study, we isolated the H3N3 virus in chickens sourced from a live poultry market in China. A comprehensive genomic analysis revealed that the virus possessed a single basic amino acid in the cleavage site of the hemagglutinin (HA) gene. Phylogenetic analysis indicated that eight genes in the H3N3 virus belong to the Eurasian lineage. Specifically, the HA and NA genes were clustered with H3N2 and H11N3, respectively, while the internal genes were closely related to the H3N8 and H9N2 viruses. Furthermore, the H3N3 virus exhibited high and moderate stability in thermal and acidic conditions and efficient replication capabilities in mammalian cells. The H3N3 virus demonstrated that it could infect and replicate in the upper and lower respiratory tract of BALB/c mice without prior adaptation, triggering hemagglutination inhibition (HI) antibody titres ranging from 80 to 160; notably, the H3N3 virus replicated vigorously within the chicken respiratory and digestive tracts. The virus also transmitted efficiently and swiftly among chickens through direct contact, leading to higher levels of HI antibodies in both the inoculated and contact birds. These findings suggest that the H3N3 virus may be a novel reassortant originating from viruses circulating in domestic poultry, thus demonstrating an increased pathogenicity and transmissibility in chickens. Our study determines that H3N3 AIV potentially threatens the poultry industry and public health, highlighting the importance of active surveillance of AIVs.