# Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database
## 3 Results and Discussion
Overall, however, our smart program identified this protein sequence as having the correct functional annotation. With the remaining examples, we show that our smart program can also be applied to protein sequences from UniProtKB/Swiss-Prot without publications tagged in the Function category as well as to protein sequences in the unreviewed UniProtKB/TrEMBL database. For the UniProtKB/Swiss-Prot protein sequences 60S ribosomal protein L5-2 (entry identifier Q8L4L4) and Protein PsiE homolog (entry identifier Q81 XB6), our model correctly classified both as negative and all their associated publications negative as well. For example, proteins from UniProtKB/TrEMBL, our model identified MEOX2 (entry identifier Q6 FIY5) as positive by predicting 18 of 20 publications as positive. The exceptions were \"Cloning of human full open reading frames in Gateway(TM) system entry vector (pDONR201)\" and \"Two candidate tumor suppressor genes, MEOX2 and SOSTDC1, identified in a 7p21 homozygous deletion region in a [PERSON] tumor\". Some publications in UniProtKB/TrEMBL are assigned to categories, but they are not manually reviewed; only the two publications listed above were assigned to the Sequences category, and our program did not think they contain function-related experimental evidence. Our model identified Beta ctenin 1 (entry identifier B1 MV73) as positive because it predicted both publications \"Equine CTNNB1 and PECAM1 nucleotide structure and expression analyses in an experimental model of normal and pathological wound repair\" and \"Genome sequence, comparative analysis, and population genetics of the domestic horse\" as positive although both these publications are tagged in the Sequences category. However, our human curator determined this to be a false positive case because the latter paper is a genome paper and the former paper looks at the expression of the gene and how it is regulated during wound repair rather than focusing on the target protein. The involvement of a human curator guarantees the accuracy of our final result after prediction by our smart program. Our model identified Maillard deglycase (entry identifier K7G4K8) as negative because it predicted both publications \"The complete mitochondrial genome of the Korean soft-shelled turtle _Peladiscus sinensis_ (Testudines, Trionychidae)\" and \"The draft genomes of soft-shell turtle and green sea turtle yield insights into the development and evolution of the turtle-specific body plan\" as negative. Our model identified Catenin (Cadherin-associated protein), beta 1, 88 kDa (entry identifier H2R2U1) as negative because it predicted both publications \"Initial sequence of the chimpanzee genome and comparison with the human genome\" and \"De novo assembly of the reference chimpanzee transcriptome from NextGen mRNA sequences\" as negative. Overall, our model correctly identified publications containing experimental evidence of protein function regardless of the publication category. As such, it can be used to identify sequences with experimental evidence of protein function. Publications associated with any annotations that have been predicted as positive are sent to a human curator for final review. Our ensemble learning approach is able to effectively identify publications that are likely to provide experimental evidence of correct protein annotation and a partial solution to the protein annotation problem.