IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning

Abstract

The development of high throughput sequencing technologies i.e. Next Generation Sequencing (NGS) is revolutionizing the exploration of cancer. Though sequence datasets are highly complex, mutation can occur randomly in DNA or RNA sequences that can make cells sicker or less fit. The unusual growth and behavior of genes in cells cause cancer. Cancer-driver gene cells grow when mutation occurs. Identification of cancer driver genes is a critical and challenging issue for researchers. In the proposed work, initially, robust features are extracted from the sequence dataset through Position Relative Incidence Matrix (PRIM) integrated with Accumulative Absolute Position Incidence Vector (AAPIV) generation. PRIM and AAPIV convert the single-dimensional sequence data into 2-dimensional numeric data. Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) are used to train the model. The proposed model is validated with different validation methods i.e., independent testing, k-fold crossvalidation, self-consistency, and jackknife testing. The proposed model predicts whether the given primary structure corresponds to cancer driver genes or not. Results analyses show 95%, 92%, and 69% accuracy on RF, Artificial Neural Networks (ANN), and SVM respectively. The comparative analysis with existing state-of-the-art models i.e., 20/20+ and Multimodal Deep Neural Network by integrating Multi-dimensional Data (NDNNMD) shows that the proposed model outperforms the existing techniques.

Response score-based protein structure analysis for cancer prediction aided by the Internet of Things

“…The observation sequences and response scores are varied accordingly in this analysis. The methods SWnet 28 , IDriveGenes 30 , and DNCL 24 are considered in this comparative analysis.…”

“…Ali et al 30 introduced a machine learning (ML) cancer driver gene prediction method. An artificial neural network (ANN) algorithm, which identifies the essential characteristics and features for gene prediction, is used in the method.…”

“…The proposed methodology is compared with the recent state-of-the-art techniques to prove its superiority compared with techniques such as SWnet 28 , IDriveGenes 30 , and DNCL 24 with the performance metrics like prediction precision, data correlation, structure change detection, correlation time, complexity, and accuracy.…”