Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data

Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge

In this paper, we develop a generic methodology to encode hierarchical causality structure among observed variables into a neural network in order to improve its predictive performance. The proposed methodology, called causality-informed neural network (CINN), leverages three coherent steps to systematically map the structural causal knowledge into the layer-to-layer design of neural network while strictly preserving the orientation of every causal relationship. In the first step, CINN discovers causal relationships from observational data via directed acyclic graph (DAG) learning, where causal discovery is recast as a continuous optimization problem to avoid the combinatorial nature. In the second step, the discovered hierarchical causality structure among observed variables is systematically encoded into neural network through a dedicated architecture and customized loss function. By categorizing variables in the causal DAG as root, intermediate, and leaf nodes, the hierarchical causal DAG is translated into CINN with a one-to-one correspondence between nodes in the causal DAG and units in the CINN while maintaining the relative order among these nodes. Regarding the loss function, both intermediate and leaf nodes in the DAG graph are treated as target outputs during CINN training so as to drive co-learning of causal relationships among different types of nodes. As multiple loss components emerge in CINN, we leverage the projection of conflicting gradients to mitigate gradient interference among the multiple learning tasks. Computational experiments across a broad spectrum of UCI data sets demonstrate substantial advantages of CINN in predictive performance over other state-of-the-art methods. In addition, an ablation study underscores the value of integrating structural and quantitative causal knowledge in enhancing the neural network's predictive performance incrementally

Networking dimensions and performance of event management ventures in Kenya

The role of networking in the sharing of knowledge and information is well documented. What is not clear, however, are the facets of networks that best drive firm performance, and whether or not the nature of business is a factor. The purpose of this study was to examine the effect of networking dimensions on the performance of event management ventures in Kenya. The researcher conceptualised that performance of ventures was a function of networking dimensions such as network capability, network structure and network dynamics. The study adopted a covariance-based confirmatory research design that sought to confirm indicators of the four variables under study, and also to establish the causal link between networking dimensions and venture performance. A population of 313 ventures was targeted, from which a sample of 288 proprietors was drawn. Using Structural Equation Modelling as the principal analysis approach, the study established that networking dimensions positively and significantly predict events venture performance. Moreover, the measurement model confirmed that the customer and learning and growth perspectives were the main indicators of events venture performance.Keywords: event management, network capability, network dynamics, network structures, structural model, venture performanc

Causation versus Prediction: Comparing Causal Discovery and Inference with Artificial Neural Networks in Travel Mode Choice Modeling

This study compares the performance of a causal and a predictive model in modeling travel mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal inference technique were used to extract the causal relationships in the mode choice decision making process and to estimate the quantitative causal effects between the variables both directly from observational data. The model results reveal that trip distance and vehicle ownership are the direct causes of mode choice in the three neighborhoods. Artificial neural network models were estimated to predict mode choice. Their accuracy was over 70%, and the SHAP values obtained measure the importance of each variable. We find that both the causal and predictive modeling approaches are useful for the purpose they serve. We also note that the study of mode choice behavior through causal modeling is mostly unexplored, yet it could transform our understanding of the mode choice behavior. Further research is needed to realize the full potential of these techniques in modeling mode choice

Manufacturer-supplier relationships and service performance in service triads

Purpose - The purpose of this paper is to explore the role of the manufacturer-supplier relationship in service performance within service triads.  Design/methodology/approach - An abductive case-research approach was adopted, using three embedded cases and 26 interviews in complex, multilevel manufacturer-supplier relationships within the same service network. Cannon and Perreault's (1999) multidimensional relationship framework was deployed to achieve granular and nuanced insight.  Findings - This study corroborates the idea that relational relationships within service triads and servitization improve performance. The role of each relationship dimension in service performance is discerned and their interplay is captured in an analytic model. Information exchange, supplier relationship-specific adaptations, and the degree of formalization of the relationship directly influence performance, while cooperative norms and operational linkages are further back in the causal ordering. The study also highlights the importance of contingent factors (the size of the service site, the proportion of its revenues coming from service contract activities) and how they affect the relationship dimensions.  Research limitations/implications - The work was conducted in one network and the findings were generalized to theory rather than additional empirical settings.  Originality/value - This study is the first to derive a contextualized causal ordering of the Cannon and Perreault (1999) framework of relationship connectors and link it with service performance

Artificial neural network analysis of teachers��� performance against thermal comfort

This is an accepted manuscript of an article published by Emerald in International Journal of Building Pathology and Adaptation on 17/04/2020, available online at: https://doi.org/10.1108/IJBPA-11-2019-0098 The accepted manuscript may differ from the final published version.Purpose: The impact of thermal comfort in educational buildings continues to be of major importance in both the design and construction phases. Given this, it is also equally important to understand and appreciate the impact of design decisions on post-occupancy performance, particularly on staff and students. This study aims to present the effect of IEQ on teachers��� performance. This study would provide thermal environment requirements to BIM-led school refurbishment projects. Design: This paper presents a detailed investigation into the direct impact of thermal parameters (temperature, relative humidity and ventilation rates) on teacher performance. In doing so, the research methodological approach combines explicit mixed-methods using questionnaire surveys and physical measurements of thermal parameters to identify correlation and inference. It was conducted through a single case study using a technical college based in Saudi Arabia. Findings: Findings from this work were used to develop a model using an Artificial Neural Network to establish causal relationships. Research findings indicate an optimal temperature range between 23��C and 25��C, with a 65% relative humidity and 0.4m/s ventilation rate. This ratio delivered optimum results for both comfort and performance