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