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Predicting disease-associated genes: Computational methods, databases, and evaluations
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DOI：10.1002/widm.1383.png)
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Abstract
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Complex diseases are associated with a set of genes (called disease genes), the identification of which can help scientists uncover the mechanisms of diseases and develop new drugs and treatment strategies. Due to the huge cost and time of experimental identification techniques, many computational algorithms have been proposed to predict disease genes. Although several review publications in recent years have discussed many computational methods, some of them focus on cancer driver genes while others focus on biomolecular networks, which only cover a specific aspect of existing methods. In this review, we summarize existing methods and classify them into three categories based on their rationales. Then, the algorithms, biological data, and evaluation methods used in the computational prediction are discussed. Finally, we highlight the limitations of existing methods and point out some future directions for improving these algorithms. This review could help investigators understand the principles of existing methods, and thus develop new methods to advance the computational prediction of disease genes. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Algorithmic Development > Biological Data Mining
Keywords:
computational method
disease gene prediction
disease-related database
machine learning
random walk
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