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

The Covid-19 pandemic has an impact on all aspects of life, including the social life of elementary school children. This study aims to determine the effectiveness of the structure learning (SLA) approach to improve teacher competence in independent elementary school students who are rejected in the KKG Kapanewon Sleman area. This research uses quantitative research with pre-experimental method with one group pretest-posttest design which is by comparing the condition before being given treatment and the condition after being given treatment. This study involved 25 teachers who were members of the teacher working group (KKG) in Kapanewon Sleman, Yogyakarta Special Region which was held in July-September 2022. This improvement used the lecture, question and answer, discussion, simulation and project methods. The data collection technique used is observation and the structure learning approach (SLA) scale. While the technical analysis of the data using the Wilcoxon Test. The Structure Learning Approach (SLA) has 5 hierarchical intervention techniques, namely; instruction, modeling, role playing, feedback, and ownwork/applying skills outside the group setting. The results of the implementation of this training based on a pre-post questionnaire showed an increase in pre-post results from an average of 28 to an average of 41.08. While the results of data processing using SPSS obtained the value of Sig. 0.05 this indicates a significant difference between the pretest and posttest. Thus, SLA can increase the independence of rejected elementary school students

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.

Bayesian Network is a knowledge representation formalism that has been proven to be valuable in gene regulatory network reconstruction. However, it is showed that the structure learning of Bayesian networks is an NP-hard problem. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, K2 algorithm has been found to be the most successful. However, the performance of K2 algorithm is greatly affected by a prior ordering of input nodes. If using inappropriate orderings, the accuracy of the learned structures will be relatively low. This paper represents a new structure learning method to reconstruct gene networks from the observational gene expression data. The proposed method is based on Binary Particle Swarm Optimization and K2 algorithm. It firstly identifies nodes ordering from the experimental data by using Binary Particle Swarm Optimization algorithm. Then, the nodes ordering information is used as an input of K2 algorithm so as to improve the performance and efficiency of the K2 algorithm in determining the network structure. Our method is evaluated using two benchmark network datasets with known structures. Experimental studies show that the proposed method can identify networks more efficiently and accurately than comparable methods reported in the literature.

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.

One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration often depends on multiple factors, including the size and density of the usually unknown underlying true graph, the sample size of the input data, and the structure learning algorithm. We propose a novel hyperparameter tuning method, called the Out-of-sample Tuning for Structure Learning (OTSL), that employs out-of-sample and resampling strategies to estimate the optimal hyperparameter configuration for structure learning, given the input dataset and structure learning algorithm. Synthetic experiments show that employing OTSL to tune the hyperparameters of hybrid and score-based structure learning algorithms leads to improvements in graphical accuracy compared to the state-of-the-art. We also illustrate the applicability of this approach to real datasets from different disciplines.

Understanding and evaluating directed acyclic graphs (DAGs) is crucial for causal discovery, particularly in high-dimensional and small-sample datasets such as microbial abundance data. This study introduces DAGMetrics, an R package designed to comprehensively evaluate and compare DAGs. The package provides descriptive and comparative metrics, streamlining the assessment of outputs from various structure learning algorithms. It was applied to datasets generated for potato tubers and soils from different terroirs (continental and island) and stages (at harvest and post-harvest). Using a comprehensive set of descriptive and comparative metrics, DAGMetrics facilitated model selection by identifying balanced and robust DAGs. The PC algorithm with Spearman correlation produced DAGs with moderate complexity and high stability across scaling and transformation setups. Additionally, the package enabled detailed exploration of the Markov blanket space, revealing small Markov blankets (up to seven nodes) and numerous isolated nodes. Identified matching edges between Markov blankets across different terroirs and stages aligned with known microbial interactions, highlighting the package’s utility in facilitating the discovery of biologically meaningful relationships. This study illustrates the utility of DAGMetrics in providing objective and reproducible tools for DAG evaluation along with its potential application in agronomic and other domains involving complex structured data.

A fully automatic feedforward neural network structural and weight learning algorithm is described. The Augmentation by Training with Residuals, ATR, requires neither guess of initial weight values nor the number of neurons in the hidden layer from users. The algorithm takes an incremental approach in which a hidden neuron is trained to model the mapping between the input and output of current exemplars, and is augmented to the existing network. The exemplars are then made orthogonal to the newly identified hidden neuron and used for the training of next hidden neuron. The improvement continues until a desired accuracy is reached. This new structural and weight learning algorithm is applied to the identification of a two-degree-of-freedom planar robot, a Van der Pol oscillator and a Mackay-Glass equation. The algorithm is shown to be effective in modeling all three systems and is far superior to a linear modeling scheme in the case of the robot.

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

Discovering ncRNA genes is a challenging problem, which has attracted much attention recently. The accuracy of computational ncRNA prediction methods still needs to be improved, however, due to the diversity and the lack of consensus patterns of ncRNA genes. In this paper, we propose an effective computational approach based on fuzzy neural networks with structure learning (FNNSL) for novel ncRNA gene prediction. It has advantages such as explicit physical meanings of nodes and parameters in the network, and effective incorporation of prior knowledge by the fuzzy sets theory. Specifically, a structure learning algorithm is presented to decrease parameter dimensions, enhance the computational efficiency, and avoid the over-learning. In addition, a fuzzy c-means clustering method is adopted for fuzzy partitioning of input feature variables, and the corresponding implementations are compared to the other ncRNA gene prediction tools. The improved prediction accuracy demonstrates the effectiveness of the proposed approach.

Ensuring fairness when training Machine Learning (ML) models remains a critical challenge, particularly when biases are embedded in the underlying data. This paper presents a fairness-aware graph structure learning framework demonstrating how learning fair graphs leads to fairer data for ML training and, consequently, fairer Artificial Intelligence (AI) decisioning based on such models. Our method incorporates a fairness regularization term into score-based structure learning algorithms, guiding the search towards graph structures that minimize discriminatory pathways while preserving statistical relationships. The learned fair graph structures enable the generation of synthetic datasets with mitigated biases, which can be used to train diverse ML models. This modification is non-trivial, as structure learning algorithms rely on local search strategies, while fairness is a global property that depends on the entire graph structure. Our framework is highly adaptable, compatible with various structure learning algorithms, and seamlessly incorporates different fairness metrics to meet specific contextual needs. Extensive experiments on both real-world and synthetic datasets demonstrate that our approach significantly improves fairness while maintaining competitive predictive performance, offering an interpretable and versatile solution for mitigating bias in AI systems..

Learning the structure of continuous-time Bayesian networks directly from data has traditionally been performed using score-based structure learning algorithms. Only recently has a constraint-based method been proposed, proving to be more suitable under specific settings, as in modelling systems with variables having more than two states. As a result, studying diverse structure learning algorithms is essential to learn the most appropriate models according to data characteristics and task-related priorities, such as learning speed or accuracy. This article proposes alternative algorithms for learning multidimensional continuous-time Bayesian network classifiers, introducing, for the first time, constraint-based and hybrid algorithms for these models. Nevertheless, these contributions also apply to the simpler one-dimensional classification problem for which only score-based solutions exist in the literature. More specifically, the aforementioned constraint-based structure learning algorithm is first adapted to the supervised classification setting. Then, a novel algorithm of this kind, specifically tailored for the multidimensional classification problem, is presented to improve the learning times for the induction of multidimensional classifiers. Finally, a hybrid algorithm is introduced, attempting to combine the strengths of the score- and constraint-based approaches. Experiments with synthetic and real-world data are performed not only to validate the capabilities of the proposed algorithms but also to conduct a comparative study of the available competitors.

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.

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.

Abstratc The development of technology that so fast and orientation nasional-internasional broad organization, requires employement has high competence. The organization of employement certainly create a new situation and put school graduates to be able to conform to the situation change. Self Adjustment is constantly interaction between individuals with him, with others, and with the environment. Self Adjustment, help students become happy and Able to devote attention and affection to the people . On the other hand , when individual unable to meet suit in work environment and he will experience conflict the inner and not optimal to his advantage. Structured Learning Approach is an approach learn structured developed based on theoretical social learning and modification behavior. This study used an experimental research design which aims to determine the effectiveness of SLA in increasing self adjustment in the working environment for students Prakerin in groups. Research subjects is 14 students prakerin at the university of kanjuruhan. The instrument used self adjustment scale developed by researchers.The research results show that (1) the average value of the pretest students before training is 32.07, (2) The average value of posttest students after the training 103.79, (3) 14 respondents have higher scores after the training than before the training, (4) the average increase after post-test was 7.50 with a significance level of 0.001 .

Computer Aided Diagnosis (CADx) systems are designed to assist doctors in medical image interpretation. However, a CADx is often thought of as a black box whose diagnostic decision is not intelligible to a radiologist. Therefore, a system that uses semantic image interpretation, and mimics human image analysis, has clear benefits. We propose a new method for automatic textual description of medical image findings, such as lesions in medical images. The method performs joint estimation of semantic features of lesions from image measurements. We formalize this problem as learning to map a set of diverse medical image measurements to a set of semantic descriptor values. We use a structured learning framework to model individual semantic descriptors and their relationships. The parameters of the model are efficiently learned using the Structured Support Vector Machine (SSVM). The proposed approach generates radiological lexicon descriptors used to make a diagnosis of various diseases. This can help radiologists easily understand a diagnosis recommendation made by an automatic system, such as CADx. We apply the proposed method to publicly available and to proprietary breast and brain imaging datasets, and show that our method generates more accurate descriptions, as compared to other alternative approaches.