Inference Tools Research Articles

Ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization.

Microbiome research is now moving beyond the compositional analysis of microbial taxa in a sample. Increasing evidence from large human microbiome studies suggests that functional consequences of changes in the intestinal microbiome may provide more power for studying their impact on inflammation and immune responses. Although 16S rRNA analysis is one of the most popular and a cost-effective method to profile the microbial compositions, marker-gene sequencing cannot provide direct information about the functional genes that are present in the genomes of community members. Bioinformatic tools have been developed to predict microbiome function with 16S rRNA gene data. Among them, PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) has become one of the most popular functional profile prediction tools, which generates community-wide pathway abundances. However, no state-of-art inference tools are available to test the differences in pathway abundances between comparison groups. We have developed ggpicrust2, an R package, for analyzing functional profiles derived from 16S rRNA sequencing. This powerful tool enables researchers to conduct extensive differential abundance analyses and generate visually appealing visualizations that effectively highlight functional signals. With ggpicrust2, users can obtain publishable results and gain deeper insights into the functional composition of their microbial communities. The package is open-source under the MIT and file license and is available at CRAN and https://github.com/cafferychen777/ggpicrust2. Its shiny web is available at https://a95dps-caffery-chen.shinyapps.io/ggpicrust2_shiny/.

Ensemble Learning Based Gene Regulatory Network Inference

In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results from different base learners have been understudied in the context of gene regulatory network inference. In this paper we applied genetic algorithms and frequent itemset mining, to design small but effective ensembles of gene regulatory network inference methods. These ensembles were evaluated and compared to well-established single and ensemble methods, on both real and synthetic datasets. Results showed that small ensembles, consisting of few but diverse base learners, enhance the exploration of the solution space, and compensate base learners biases, outperforming state-of-the-art methods. Results advocate for the use of such methods as gene regulatory network inference tools.

AM fungal-bacterial relationships: what can they tell us about ecosystem sustainability and soil functioning?

Considering our growing population and our continuous degradation of soil environments, understanding the fundamental ecology of soil biota and plant microbiomes will be imperative to sustaining soil systems. Arbuscular mycorrhizal (AM) fungi extend their hyphae beyond plant root zones, creating microhabitats with bacterial symbionts for nutrient acquisition through a tripartite symbiotic relationship along with plants. Nonetheless, it is unclear what drives these AM fungal-bacterial relationships and how AM fungal functional traits contribute to these relationships. By delving into the literature, we look at the drivers and complexity behind AM fungal-bacterial relationships, describe the shift needed in AM fungal research towards the inclusion of interdisciplinary tools, and discuss the utilization of bacterial datasets to provide contextual evidence behind these complex relationships, bringing insights and new hypotheses to AM fungal functional traits. From this synthesis, we gather that interdependent microbial relationships are at the foundation of understanding microbiome functionality and deciphering microbial functional traits. We suggest using pattern-based inference tools along with machine learning to elucidate AM fungal-bacterial relationship trends, along with the utilization of synthetic communities, functional gene analyses, and metabolomics to understand how AM fungal and bacterial communities facilitate communication for the survival of host plant communities. These suggestions could result in improving microbial inocula and products, as well as a better understanding of complex relationships in terrestrial ecosystems that contribute to plant-soil feedbacks.

Field-level Neural Network Emulator for Cosmological N-body Simulations

We build a field-level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output the nonlinear displacements and velocities of N-body simulation particles based on their linear inputs. Cosmology dependence is encoded in the form of style parameters at each layer of the neural network, enabling the emulator to effectively interpolate the outcomes of structure formation between different flat Lambda cold dark matter cosmologies over a wide range of background matter densities. The neural network architecture makes the model differentiable by construction, providing a powerful tool for fast field-level inference. We test the accuracy of our method by considering several summary statistics, including the density power spectrum with and without redshift space distortions, the displacement power spectrum, the momentum power spectrum, the density bispectrum, halo abundances, and halo profiles with and without redshift space distortions. We compare these statistics from our emulator with the full N-body results, the COmoving Lagrangian Acceleration (COLA) method, and a fiducial neural network with no cosmological dependence. We find that our emulator gives accurate results down to scales of k ∼ 1 Mpc−1 h, representing a considerable improvement over both COLA and the fiducial neural network. We also demonstrate that our emulator generalizes well to initial conditions containing primordial non-Gaussianity without the need for any additional style parameters or retraining.

Concerns regarding the proposal for an ecological equation of state: an assessment starting from the organic biophysics of ecosystems (OBEC).

The goal of testing the theoretical fruitfulness and empirical utility of the links between ecology and thermodynamics has been elusive. This could explain the breakdown of ecology into multiple branches, some of them intended to develop models in agreement with the principles of physics. The maximum entropy algorithm (MaxEnt) is one of the most frequently mentioned topics in this field. Within the MaxEnt framework, a quantitative relationship between various ecological parameters has recently been proposed as a seeming ecological equation of state (EESH; Harte et al. 2022. An equation of state unifies diversity, productivity, abundance and biomass. Commun. Biol. 5: 874). We analyze the EESH from the interdisciplinary perspective of Organic Biophysics of Ecosystems (OBEC). Consistent with this analysis, the EESH neglects the analytical similarity between key ecological variables and statistical mechanical variables, it does not include any intensive variable useful to determine the distance of ecological systems from equilibrium, it does not involve any constant useful to define the statistical range within which the system can be considered out of danger despite widespread effects of anthropogenic impact, and its general structure bears no resemblance to previous equations of state because it is based on a subjective approach devoid of physical content that is only useful as a tool for statistical inference. So, our conclusions are: (i) the EESH does not withstand comparison with prior knowledge and empirical evidence from both ecology and physics, and (ii) it cannot be considered an ecological equation of state.

LinguaPhylo: A probabilistic model specification language for reproducible phylogenetic analyses.

Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, 'LPhy', which is designed to be both human and machine-readable. A graphical user interface accompanies 'LPhy', allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results.

Quantifying Causal Path-Specific Importance in Structural Causal Model

Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the score defined in this paper can quantify the impact of a decision variable on an outcome variable along a specific pathway at the individual level. Moreover, the score has many desirable properties, including following the chain rule and being consistent. Finally, this paper presents an algorithm that can leverage these properties and find the k-most important paths with the highest importance scores in a causal graph effectively.

Application of Mahalanobis Distance in Education and Educational Psychology: A Mini Review

Mahalanobis distance is a metric for the distance between groups regarding specific criteria. Mahalanobis distance is a measurement between two data sets in the space delineated by specific attributes. It will accurately assess the distance by giving various essential factors to the characteristics of data points since it considers variances and correlations between characters. Mahalanobis distance has an essential use in education in terms of achievement analysis. Mahalanobis distance is a powerful tool for drawing inferences between two groups of several variables. As it is a dimensionless, single number, it is easy to understand and interpret. Since this is a powerful tool, the application of Mahalanobis distance will increase rapidly to draw the inference. The paper examines the literature on Mahalanobis distance in education and its massive potential in educational psychology since 2020.

CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics.

Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks. The source code and data is available at https://github.com/MihirBafna/CLARIFY.

Deep electron cloud‐activity and field‐activity relationships

AbstractChemists have been pursuing general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure‐activity relationship (QSAR) models have limited application domains; for example, they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This paper attempts to develop a new QSAR method that is theoretically possible to predict various properties of molecules with diverse structures. The proposed deep electron cloud‐activity relationships (DECAR) and deep field‐activity relationships (DFAR) methods consist of three essentials: (1) a large number of molecule entities with activity data as training objects and responses; (2) three‐dimensional electron cloud density (ECD) or related field data by the accurate density functional theory methods as input descriptors; and (3) a deep learning model that is sufficiently flexible and powerful to learn the large data described above. DECAR and DFAR are used to distinguish 977 sweet and 1965 non‐sweet molecules (with 6‐fold data augmentation), and the classification performance is demonstrated to be significantly better than the traditional least squares support vector machine (LS‐SVM) models using traditional descriptors. DECAR and DFAR would provide a possible way to establish a widely applicable, cumulative, and shareable artificial intelligence‐driven QSAR system. They are likely to promote the development of an interactive platform to collect and share the accurate ECD and field data of millions of molecules with annotated activities. With enough input data, we envision the appearance of several deep networks trained for various molecular activities. Finally, we could anticipate a single DECAR or DFAR network to learn and infer various properties of interest for chemical molecules, which will become an open and shared learning and inference tool for chemists.

A comparative analysis of the chromosomes of three FARQ species complex members, Ceratitis rosa, C. quilicii, and C. fasciventris F2 (Diptera: Tephritidae).

The Ceratitis FARQ species complex consists of four highly destructive agricultural pests of Africa, namely C. fasciventris, C. anonae, C. rosa, and C. quilicii. The members of the complex are considered very closely related and the species limits among them are rather obscure. Their economic significance and the need for developing biological methods for their control makes species identification within the complex an important issue, which has become clear that can only be addressed by multidisciplinary approaches. Chromosomes, both mitotic and polytene, can provide a useful tool for species characterization and phylogenetic inference among closely related dipteran species. In the current study, we present the mitotic karyotype and the polytene chromosomes of C. rosa and C. quilicii together with in situ hybridization data. We performed a comparative cytogenetic analysis among the above two species and C. fasciventris, the only other cytogenetically studied member of the FARQ complex, by comparing the mitotic complement and the banding pattern of the polytene chromosomes of each species to the others, as well as by studying the polytene chromosomes of hybrids between them. Our analysis revealed no detectable chromosomal rearrangements discriminating the three FARQ members studied, confirming their close phylogenetic relationships.

On a wider class of prior distributions for graphical models

AbstractGaussian graphical models are useful tools for conditional independence structure inference of multivariate random variables. Unfortunately, Bayesian inference of latent graph structures is challenging due to exponential growth of $\mathcal{G}_n$ , the set of all graphs in n vertices. One approach that has been proposed to tackle this problem is to limit search to subsets of $\mathcal{G}_n$ . In this paper we study subsets that are vector subspaces with the cycle space $\mathcal{C}_n$ as the main example. We propose a novel prior on $\mathcal{C}_n$ based on linear combinations of cycle basis elements and present its theoretical properties. Using this prior, we implement a Markov chain Monte Carlo algorithm, and show that (i) posterior edge inclusion estimates computed with our technique are comparable to estimates from the standard technique despite searching a smaller graph space, and (ii) the vector space perspective enables straightforward implementation of MCMC algorithms.

Early assessment of cardiomyopathy in Duchenne patients by means of longitudinal strain echocardiography.

The diagnosis of Duchenne-linked cardiomyopathy may be challenging. Conventional echocardiographic measurements typically show deterioration beyond the second decade. Global longitudinal strain has been proposed as an earlier marker than left ventricular ejection fraction. A prospective, observational, cross-sectional, case-control study was carried out. Both Duchenne patients and control subjects underwent transthoracic echocardiogram in order to assess left ventricle function. Bayesian linear regression was the main tool for inference. Age effects were parameterised by means of a spline function because of its simplicity to characterise continuous variables and smooth contributions. The posterior distribution of the marginal age effects was used to assess the earliest age of deterioration of each marker. Sixteen Duchenne patients and twenty-two healthy male subjects were enrolled. On overage, cardiac function measures were found for ejection fraction and longitudinal strain among different groups. Age effects on global longitudinal strain are more reliably found at ages of 6 and above, while ejection fraction starts to deteriorate at an older age. Progressive left ventricular dysfunction in Duchenne patients is one of the key issues and starts at an early age with subtle symptoms. This cross-sectional study provides supporting evidence that global longitudinal strain is an earlier marker of disease progression than ejection fraction in Duchenne patients.

Introducing the New Editorial Team

Introducing the New Editorial Team

Нечіткі дані у семантичних wiki-ресурсах: моделі, джерела та методи обробки

We analyze main types of dirty data processed by intelligente information systems, criteria of data classification and means of detection non-classical properties of data. Results of this analysis are represented by ontological model that contains taxonomy of classical and nonclassical data and knowledge-oriented methods of their transformation. Special attention is paid to semantically incorrect data that corresponds to vague knowledge. This ontological model intended to provide more effectively methods for transforming raw data into smart data suitable for automatic analysis, knowledge acquisition and reuse in other information systems. The ontological approach provides integration of the proposed model with other external ontologies that formalize characteristics of various methods and software tools that can be used fo data analysis (data mining, inductive inference, semantic queries, and instrimental tools for testing various aspects of the ontology quality, etc.). The work uses the experience of knowledge base developing of the portal version of the Great Ukrainian Encyclopedia e-VUE. This information resource is based on the semantic Wiki technology, it has a large volume, a complex structure and contains a large number of various heterogeneous information objects. Wiki resources are interesting from the point of view of collaborative processing the fuzzy data that describe heterogeneous information objects and knowledge structures. Due to the fact that the creation of this information resource involves a large number of specialists of various scientific fields, who have different areas of expertise and qualifications in use of knowledge-oriented technologies, there are many differences in the understanding of the rules for presenting and structuring data, and therefore a significant part of the Encyclopedia content needs additional verification of its correctness. Therefore, we need in formalized and scalable solutions for detection and processing various types of inconsistence, incompleteness and semantic incorrectness of data. The proposed approach can be useful for the creation of other large-scale resources based on both the semantic Wiki technology and other technological platforms for collaborative processing of distributed data and knowledge.

Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations.

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths.

EvoPhylo: An r package for pre‐ and postprocessing of morphological data from relaxed clock Bayesian phylogenetics

Abstract Relaxed clock Bayesian evolutionary inference (BEI) enables the co‐estimation of phylogenetic trees and evolutionary parameters associated with models of character and lineage evolution. Fast advances in new model developments over the past decade have boosted BEI as a major macroevolutionary analytical framework using morphological and/or molecular data across vastly different study systems. However, there is limited availability of bioinformatic tools to pre‐ and postprocess data from BEI, such as identifying morphological data partitions, or statistically testing and creating publication quality plots of evolutionary hypotheses. Here, we introduce EvoPhylo, an r package to perform automated morphological character partitioning and analyse macroevolutionary parameter from relaxed clock (time‐calibrated) BEI outputted by the programs Mr.Bayes and BEAST2. These include rates of evolution and mode of selection for each character partition, diversification rate parameters, and handling fossil‐only posterior trees. We present the theoretical background behind EvoPhylo's functions and analytical tools for evolutionary hypothesis testing, its potential uses, and interpretation of its results with a series of vignettes and links to a step‐by‐step tutorial using examples from two empirical datasets. EvoPhylo will facilitate the use of Bayesian relaxed clocks as a tool for macroevolutionary inference across a wide range of users and fields of research, especially those that make usage of morphological datasets, from paleontological to total evidence dating analyses.

Beyond Co-integration: New Tools for Inference on Co-movements

Abstract Macroeconomic and aggregate financial series were shown empirically to share an unconventional form of cyclical and persistent dynamics, whose functional form was obtained from the solution of general-equilibrium models with heterogeneous firms. The econometric modeling of equations that link such series requires a new methodology, as existing parametric techniques can cause paradoxical regression results and omit predictabilities. We provide a solution to disentangle the genuine relation between variables (the parameters linking them) from the unconventional dynamics that drive them. As an application, we show that GBP-USD forward premia have no predictive power for excess returns over 1976–2015 (thus solving this forward-premium puzzle) once the unconventional dynamics of spot rates are modeled. Taking advantage of these dynamics, we uncover a trading strategy which consistently outperforms existing ones in the out-of-sample period 2015–2021, delivering almost treble their profits and yielding a Sharpe ratio of 85%. Hence, even in this heavily traded market, the efficient market hypothesis has been failing for over 45 years as persistent profit opportunities remained unexploited because of the unconventional dynamics of the spot rate.

Karakteristik Manajemen dan Kepemimpinan Transformasional sebagai Penentu Kreativitas Karyawan yang Dimediasi oleh Kepuasaan Kerja

<em>Many organizational top managers are increasingly aware of the importance of innovation as a precondition to maintaining sustainable organizational growth. Moreover, creativity is also often seen as the main driving force for innovation. The aim of this study is to analyze the effects of management characteristics represented by the dimensions of supervisor’s humility and abusive supervision, and transformational leadership on employee creativity mediated by employee job satisfaction. Utilizing a purposive sampling method, this study selected 260 back-office employees who work at banks in Indonesia and the online survey was administered during the period of June to July 2022. Employing PLS-SEM as an inferential analysis tool, this study found that a supervisor’s humility and transformational leadership have positive effects, whereas abusive supervision has a negative effect on employees’ job satisfaction. Furthermore, employee job satisfaction was also found to mediate the effects of supervisor’s humility, abusive supervision, and transformational leadership towards employee creativity.</em>

Comparative evaluation of the MAPlex, Precision ID Ancestry Panel, and VISAGE Basic Tool for biogeographical ancestry inference

Biogeographical ancestry (BGA) inference from ancestry-informative markers (AIMs) has strong potential to support forensic investigations. Over the past two decades, several forensic panels composed of AIMs have been developed to predict ancestry at a continental scale. These panels typically comprise fewer than 200 AIMs and have been designed and tested with a limited set of populations. How well these panels recover patterns of genetic diversity relative to larger sets of markers, and how accurately they infer ancestry of individuals and populations not included in their design remains poorly understood. The lack of comparative studies addressing these aspects makes the selection of appropriate panels for forensic laboratories difficult. In this study, the model-based genetic clustering tool STRUCTURE was used to compare three popular forensic BGA panels: MAPlex, Precision ID Ancestry Panel (PIDAP), and VISAGE Basic Tool (VISAGE BT) relative to a genome-wide reference set of 10k SNPs. The genotypes for all these markers were obtained for a comprehensive set of 3957 individuals from 228 worldwide human populations. Our results indicate that at the broad continental scale (K=6) typically examined in forensic studies, all forensic panels produced similar genetic structure patterns compared to the reference set (G'≈90%) and had high classification performance across all regions (average AUC-PR > 97%). However, at K=7 and K=8, the forensic panels displayed some region-specific clustering deviations from the reference set, particularly in Europe and the region of East and South-East Asia, which may be attributed to differences in the design of the respective panels. Overall, the panel with the most consistent performance in all regions was VISAGE BT with an average weighted AUC̅W score of 96.26% across the three scales of geographical resolution investigated.