Disease Prediction using Gene Data Over Data Mining and Artificial Intelligence Techniques: A Survey
Keywords:
Gene data, accuracy, machine learning, healthcare, disease predictionAbstract
Medical research has investigated Disease Prediction (DP) based on Gene data to a key level today, where the DP is attained by using Data Mining (DM) and Artificial Intelligence (AI) to detect disease-related genes. The traditional methods, such as Genome-Wide Association Studies (GWAS) and Linkage Analysis (LA), typically generate several positional candidate genes; experimental validation is cost-effective and in a time frame. Once Gene Prioritization (GP) methods have been included in computational means such as Feature Selection (FS), clustering, and Machine Learning (ML), GP has been significantly enhanced. Using Deep Learning (DL), Support Vector Machines (SVM), or ensemble classifiers, AI supports the improvement of predicting accuracy based on the learning of fine-grain patterns from genomic datasets. Network-based methods such as protein-protein interaction (PPI) networks and gene ontology (GO) analysis help us to recognize disease-gene predictions (DGP). Next Generation Sequencing (NGS) presents massive genomic data subject to efficient pre-processing and dimensionality reduction methods to mitigate high-dimensionality problems. The DL is used to retrieve the hidden relationships in genomic databases that, in turn, help toward the early disease diagnosis. The developments in integrating heterogeneous genomic data and dealing with biases in training datasets have not yet been attained. This analysis classifies computational tools for gene DP in terms of conceptual model rather than technical method and presents recent works in AI-based genomic research proof towards precision medicine and personal healthcare.
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