Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

Beyond Covariation: Cues to Causal Structure

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning

Examining the dynamics of macroeconomic indicators and banking stock returns with bayesian networks

According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods