A Hybrid Model of Cancer Diseases Diagnosis Based on Gene Expression Data with Joint Use of Data Mining Methods and Machine Learning Techniques

Abstract

One of the current focuses of modern bioinformatics is the development of hybrid models to process gene expression data, in order to create diagnostic systems for various diseases. In this study, we propose a solution to this problem that combines an inductive spectral clustering algorithm, random forest classifier, convolutional neural network, and alternative voting method for making the final decision about patient condition. In the first stage, we apply the spectral clustering algorithm to gene expression profiles using inductive methods of objective clustering, with the calculation of internal, external, and balance clustering quality criteria. This results in clusters of mutually correlated and differently expressed gene expression profiles. In the second stage, we apply the random forest classifier and convolutional neural network to identify the examined objects, containing as attributes the gene expression values in the allocated clusters. The presented research solves both binary- and multi-classification tasks. The final decision about the patient’s condition is made using the alternative voting method, considering the classification results based on the gene expression data in various clusters. The simulation results showed that the proposed technique was highly effective, achieving a high accuracy in object identification when both classifiers were used. However, the convolutional neural network had a significantly higher data processing efficiency than the random forest algorithm, due to its substantially shorter processing time.

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

“…In this study, we extend our previous research on the application of deep learning methods for gene expression data processing, aiming to develop and enhance cancer disease diagnosis systems [11][12][13]. The main contributions of the current research are as follows:…”

“…The gene expression value of a sample, as outlined in Table 1, signifies its activity level, indicative of the intensity of the associated protein synthesis process, and is proportionate to the volume of similar genes. In compliance with the methodology detailed in [11,13], firstly, absolute gene counts were converted into a more conducive range (Count Per Million-CPM) using the subsequent formula:…”

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

“…The plantar flexors (PF) correspond to the swing-leg pushing the center-of-mass forward, and the PF correspond to the stance-leg producing opposing torque. The study conducted in [ 8 ] demonstrated that the opposing forces produced by PF can persuade freezing, and it also explained the gait irregularities that are closer to freezing, such as step length reduction and irregular walking patterns.…”

“…They also include a convolutional autoencoder and transfer learning-based scheme for Alzheimer’s disease visualization [ 53 ]; a perceptron neural network for bacterial behavior programming [ 54 ]; and a deep neural architecture with generative adversarial network for brain tumor classification [ 55 ]. In addition, they include a deep neural network for epidemic prediction of COVID disease [ 56 ]; deep learning for sequential analysis of biomolecules [ 57 ]; elastic net and neural networks for the identification of plant genomics [ 58 ]; data mining and machine learning algorithms based on spectral clustering, random forest, and neural networks for cancer diagnosis through gene data [ 8 ]; and a stacking ensemble model based on an auto-regressive integrated moving average, exponential smoothing, a neural network autoregressive, a gradient-boosting regression tree, and extreme gradient boost models for infectious diseases [ 9 ]. Finally, there are supervised machine learning algorithms for lung disease detection, respiratory sound analyses, and so on [ 10 ].…”

“…For instance, in [ 7 ], Anninou et al developed a mathematical model for PDI by exploiting the concept of fuzzy cognitive maps, and a generic algorithm was proposed based on nonlinear Hebbian learning techniques. Recently, a wide use of artificial intelligence methodologies for modeling different diseases has emerged [ 8 , 9 , 10 ], but, as per our exhaustive search, these methodologies are not yet exploited to study the dynamics of PDI. Thus, it seems promising to exploit the well-established strength of machine learning and artificial intelligence techniques to study the dynamics of PDI.…”