Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data.

Graphical models represent conditional independence relationships between variables, including, for example, those between the various symptoms and causes of disease. An important topic in the area of machine learning is learning these types of models from data. In some applications, including information that is not contained in the data, i.e. prior information, is crucial. This paper describes an algorithm for learning Bayesian network (BN) structure from data using prior knowledge. The aim of this research is to design an efficient algorithm that utilises prior knowledge in a manner that allows users to express what they know about the problem domain. In this aspect, we developed local search algorithm using prior knowledge (LSPK). This paper proposed algorithms for Bayesian network structure learning, which is described as an optimisation problem. A search-and-score approach was followed, which built upon a Local move algorithm and using the Bayesian Dirichlet likelihood equivalence (BDe) as a scoring metric. We demonstrated the effect of including ancestor relations prior knowledge and conditional independence. The results show that the effects of prior knowledge typically had a positive effect on learning.