# Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database
In our work, we use BioWordVec [16], a word embedding method that captures subword information, i.e., each word is further represented as a bag of n-gram characters [17], and exploits the internal structure of words to enrich feature representation in the numeric vector. Once such an effective feature representation of a text input is available, it can be widely used for classification in deep learning models such as recurrent neural networks(RNNs) [18] and convolutional neural networks (CNNs) [19]. In RNNs, a sequence is broken into multiple words and the output is passed from the previous layer of the network to the current layer; in CNNs, a matrix is formed using word embeddings, and then convolutions using filters are performed on the matrix to generate feature maps. For our work, we chose to use a recurrent convolutional neural network (RCNN) to better capture contextual information and discriminative phrases using max pooling (a discretization process) and filtering [20]. Given that ensemble learning works well when errors for the individual models are uncorrelated, we also use two discriminative classifiers, logistic regression and a support vector machine (SVM) with a linear kernel. Logistic regression is efficient for high-dimensional inputs, and regularization can be used to avoid overfitting. Similarly, regularization can be used with the SVM model. For the logistic regression and SVM models, we use BioSentVec [21] rather than BioWordVec. The former is trained on large-scale biomedical texts to generate sentence embeddings and is more efficient for the traditional models. To demonstrate the effectiveness of our ensemble learning method, we compare its performance with the state-of-the-art Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) fine-tuned using the same data for the same task. The Bidirectional Encoder Representations from Transformers (BERT) model is a contextualized word representation model based on a masked language model, which predicts masked words randomly in a sequence [22]. It learns information from both the left and right contexts of an input token during training and has been proven to achieve state-of-the-art performance for most NLP tasks. BioBERT [23] is a biomedical-domain language representation model pre-trained on large-scale biomedical corpora (PubMed abstracts and PMC full-text articles), which extends BERT to the biomedical domain. In an effort to ameliorate protein misannotation, we developed a smart software program based on natural language processing (NLP), which periodically queries the UniProtKB database for peer-reviewed articles on a given protein annotation. Word embedding using BioWordVec or BioSentVec is used on all pertinent publication titles retrieved, and an ensemble model is used to determine whether the publications demonstrate laboratory validation of protein function for a given protein annotation. Each of the three models used for ensemble learning--RCNN, logistic regression, and SVM--is individually trained using positive and negative data, and voting is used to classify whether a publication provides the necessary experimental confirmation. Annotations of proteins of which functions have been confirmed experimentally in peer reviewed publications are considered validated. Importantly, because we want to ensure the maximum number of verifications and because the results are reported to a human curator for confirmation, we use recall as our foremost metric. In this paper, we describe a smart program used to verify protein annotation based on publications in the UniProtKB database and, in particular, classifying publications without any evidence tag for experimental validation, the majority of which are in the uncurated UniProtKB/TrEMBL database. While this smart program is the key to the success of our project, it is only a part of it. In the near future, we will present a system for individual researchers to use to assist them with annotating protein sequences of interest to them.