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With the rapid growth in high-throughput biological sequencing technologiesand subsequently the amount of produced omics data, it is essential to developautomated methods to annotate the functionality of unknown genes and proteins.There are developed tools such as AHRD applying known proteins characterizationto annotate unknown ones. Some other algorithms such as eggNOG applyorthologous groups of proteins to detect the most probable function. However,while the available tools focus on the detection of the most similarcharacterization, they are not able to generalize and integrate informationfrom multiple homologs while maintaining accuracy. Here, we devise AFDP, anintegrated approach for protein function prediction which benefits from thecombination of two available tools, AHRD and eggNOG, to predict thefunctionality of novel proteins and produce more precise human readabledescriptions by applying our stCFExt algorithm. StCFExt creates functiondescriptions applying available manually curated descriptions in swiss-prot.Using a benchmark dataset we show that the annotations predicted by ourapproach are more accurate than eggNOG and AHRD annotations.

AFDP: An Automated Function Description Prediction Approach to Improve Accuracy of Protein Function Predictions