A review of computational approaches to predict gene functions

Recently, novel high-throughput biotechnologies have provided rich data about different genomes. However, manual annotation of gene function is time consuming. It is also very expensive and infeasible for the growing amounts of data. At present there are numerous functions in certain species that remain unknown or only partially known. Hence, the use of computational approaches to predicting gene function is becoming widespread. Computational approaches are time saving and less costly. Prediction analysis provided can be used in hypotheses to drive the biological validation of gene function.

Objective: This paper reviews computational approaches such as the support vector machine, clustering, hierarchical ensemble and network-based approaches.

Methods: Comparisons between these approaches are also made in the discussion portion.

Results: In addition, the advantages and disadvantages of these computational approaches are discussed.

Conclusion: With the emergence of omics data, the focus should be continued on integrating newly added data for gene functions prediction field.