Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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Author: search.filters.author.Dobreva, JovanaSubject: search.filters.subject.Knowledge extraction

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    Item type:Publication,
    DD-RDL: Drug-Disease Relation Discovery and Labeling
    (Springer International Publishing, 2022-04-12)
    Dobreva, Jovana
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    Drug repurposing, which is concerned with the study of the effectiveness of existing drugs on new diseases, has been growing in importance in the last few years. One of the core methodologies for drug repurposing is text-mining, where novel biological entity relationships are extracted from existing biomedical literature and publications, whose number skyrocketed in the last couple of years. This paper proposes an NLP approach for drug-disease relation discovery and labeling (DD-RDL), which employs a series of steps to analyze a corpus of abstracts of scientific biomedical research papers. The proposed ML pipeline restructures the free text from a set of words into drug-disease pairs using state-of-the-art text mining methodologies and natural language processing tools. The model’s output is a set of extracted triplets in the form (drug, verb, disease), where each triple describes a relationship between a drug and a disease detected in the corpus. We evaluate the model based on a gold standard dataset for drug-disease relationships, and we demonstrate that it is possible to achieve similar results without requiring a large amount of annotated biological data or predefined semantic rules. Additionally, as an experimental case, we analyze the research papers published as part of the COVID-19 Open Research Dataset (CORD-19) to extract and identify relations between drugs and diseases related to the ongoing pandemic.
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    Item type:Publication,
    Improving NER Performance by Applying Text Summarization on Pharmaceutical Articles
    (Springer International Publishing, 2020-10-30)
    Dobreva, Jovana
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    Jofche, Nasi
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    Analyzing long text articles in the pharmaceutical domain, for the purpose of knowledge extraction and recognizing entities of interest, is a tedious task. In our previous research efforts, we were able to develop a platform which successfully extracts entities and facts from pharmaceutical texts and populates a knowledge graph with the extracted knowledge. However, one drawback of our approach was the processing time; the analysis of a single text source was not interactive enough, and the batch processing of entire article datasets took too long. In this paper, we propose a modified pipeline where the texts are summarized before the analysis begins. With this, the source articles is reduced significantly, to a compact version which contains only the most commonly encountered entities. We show that by reducing the text size, we get knowledge extraction results comparable to the full text analysis approach and, at the same time, we significantly reduce the processing time, which is essential for getting both real-time results on single text sources, and faster results when analyzing entire batches of collected articles from the domain.