UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications

Abstract

<jats:title>Abstract</jats:title><jats:p>In the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliography in UniProt, we investigate a Convolution Neural Network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge to categorize publications at the accession annotation level is that the same publication can be annotated with multiple proteins, and thus be associated to different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achieved a F1-score of 0.72, outperforming baseline models based on logistic regression and support vector machine by up to 22 and 18 percentage points, respectively. We believe that such approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators to decide whether a publication is relevant for further curation for a protein accession.</jats:p>

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature

“…18.524571 doi: bioRxiv preprint Automatic text classification appears as an essential methodology to ensure high quality of living evidence updates. Text classification consists of assigning categorical labels to a given text passage (e.g., an abstract) based on its similarity to the existing labeled examples (20)(21)(22). Classical text classifiers use statistical document representations, in which the relevance of a word to a document is proportional to its frequency in the document and inversely proportional to its frequency in the collection (the so-called term frequency-inverse document frequency (tf-idf) framework), to create a vectorial representations of the documents (23).…”