State-of-the-Art in Candidate Disease Genes Prioritization and Prediction Approaches and Techniques

Abstract

Objective: Candidate disease genes identification has grasped the attention of many researchers for its significant role in bioinformatics. In this review, we demonstrate several classifications of some recent identification approaches and their datasets. Methods/Findings: The approaches are classified into five categories and the datasets into two categories. Some categories are also classified into several types. In each category, we explain every approach based on its objectives, mechanism, datasets and results. Different algorithms have been used such as random walk algorithm, machine-learning algorithms or genetic algorithm. Furthermore, the common approach followed to test the performance is cross-validation approach using precision, recall and F1-metrics. During our research, we found a novelty of many methods and a noticeable improvement in some networks and algorithms. We also noticed that the major emphasis was to enhance genome datasets using different mechanisms such as integrating them or adding new features. We noticed that most researchers focus more on this aspect as they believe that the best way to improve genes prioritization and identification and get more accurate results is to have a reliable dataset including all required information. Application: This survey can be a valuable source of information. It explains and summaries every item in the classification in a simple and understandable way. Therefore, it can be used by researchers concerning with disease genes identification as it can enlighten and guide them to different techniques and dataset used in this subject. Keywords: Biological and Topological Properties, Disease Gene Identification, Gene Expression, Gene Ontology, Phenotype, Protein Interaction Networks