Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

Deep learning‐based microarray cancer classification and ensemble gene selection approach

Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k‐nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis‐related brain tissue lesions were examined to show the generalisability of the model method.

Engineering mechanobiology through organoids-on-chip: A strategy to boost therapeutics

The mechanical environment of living cells is as critical as chemical signaling. Mechanical stimuli play a pivotal role in organogenesis and tissue homeostasis. Unbalances in mechanotransduction pathways often lead to diseases, such as cancer, cystic fibrosis, and neurodevelopmental disorders. Despite its inherent relevance, there is a lack of proper mechanoresponsive in vitro study systems. In this context, there is an urge to engineer innovative, robust, dynamic, and reliable organotypic technologies to better connect cellular processes to organ-level function and multi-tissue cross-talk. Mechanically active organoid-on-chip has the potential to surpass this challenge. These systems converge microfabrication, microfluidics, biophysics, and tissue engineering fields to emulate key features of living organisms, hence, reducing costs, time, and animal testing. In this review, we intended to present cutting-edge organ-on-chip platforms that integrate biomechanical stimuli as well as novel multicellular culture, such as organoids. We focused on its application in two main fields: precision medicine and drug development. Moreover, we also discussed the state of the art for the development of an engineered model to assess patient-derived tumor organoid metastatic potential. Finally, we highlighted the current drawbacks and emerging opportunities to match the industry needs. We envision the use of mechanoresponsive organotypic-on-chip microdevices as an indispensable tool for precision medicine, drug development, disease modeling, tissue engineering, and developmental biology.

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription. It is the flow of information from DNA to RNA with enzymes' help, and the end product is proteins and other biochemical molecules. Many technologies can capture Gene Expression from the DNA or RNA. One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. The issue in handling such a heavyweight dataset is that the learning model will be over-fitted. This problem should be addressed by reducing the dimension of the data source to a considerable amount. In recent years, Machine Learning has gained popularity in the field of genomic studies. In the literature, many Machine Learning-based Gene Selection approaches have been discussed, which were proposed to improve dimensionality reduction precision. This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. This study also lists out and briefly discusses the open issues in handling the high-dimension and less sample size data.

Identification of Differentially Expressed Genes to Establish New Biomarker for Cancer Prediction

The goal of the human genome project is to integrate genetic information into different clinical therapies. To achieve this goal, different computational algorithms are devised for identifying the biomarker genes, cause of complex diseases. However, most of the methods developed so far using DNA microarray data lack in interpreting biological findings and are less accurate in disease prediction. In the paper, we propose two parameters risk_factor and confusion_factor to identify the biologically significant genes for cancer development. First, we evaluate risk_factor of each gene and the genes with nonzero risk_factor result misclassification of data, therefore removed. Next, we calculate confusion_factor of the remaining genes which determines confusion of a gene in prediction due to closeness of the samples in the cancer and normal classes. We apply nondominated sorting genetic algorithm (NSGA-II) to select the maximally uncorrelated differentially expressed genes in the cancer class with minimum confusion_factor. The proposed Gene Selection Explore (GSE) algorithm is compared to well established feature selection algorithms using 10 microarray data with respect to sensitivity, specificity, and accuracy. The identified genes appear in KEGG pathway and have several biological importance.

A novel feature ranking method for prediction of cancer stages using proteomics data

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice.

Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data

This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR)-based method for redefining the criterion function of f-information (FI) to identify the potential genes without discretizing the continuous gene expression values. The embedded procedure is implemented by means of a water swirl algorithm (WSA), which attempts to optimize the rule set and membership function required to classify samples using a fuzzy-rule-based multiclassification system (FRBMS). Two novel update equations are proposed in WSA, which have better exploration and exploitation abilities while designing a self-learning FRBMS. The efficiency of our new approach was evaluated on 13 multicategory and 9 binary datasets of cancer gene expression. Additionally, the performance of the proposed FRFI-WSA method in designing an FRBMS was compared with existing methods for gene selection and optimization such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony algorithm (ABC) on all the datasets. In the global cancer map with repeated measurements (GCM_RM) dataset, the FRFI-WSA showed the smallest number of 16 most relevant genes associated with cancer using a minimal number of 26 compact rules with the highest classification accuracy (96.45%). In addition, the statistical validation used in this study revealed that the biological relevance of the most relevant genes associated with cancer and their linguistics detected by the proposed FRFI-WSA approach are better than those in the other methods. The simple interpretable rules with most relevant genes and effectively classified samples suggest that the proposed FRFI-WSA approach is reliable for classification of an individual’s cancer gene expression data with high precision and therefore it could be helpful for clinicians as a clinical decision support system.