The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area - in mammography, in addition to the creation of the list of similar images - cases. The created list is used for assessing the nature of the finding - whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.

• Klíčová slova
• Fuzzy S-tree, Image classification, Image comparison, Medical image, NCD, TF-IDF, Vector quantization,
• MeSH
• algoritmy MeSH
• fuzzy logika * MeSH
• komprese dat MeSH
• lidé MeSH
• mamografie metody   MeSH
• rozpoznávání automatizované metody   MeSH
• systémy pro podporu klinického rozhodování organizace a řízení   MeSH
• ukládání a vyhledávání informací metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Research of the techniques for effective fake news detection has become very needed and attractive. These techniques have a background in many research disciplines, including morphological analysis. Several researchers stated that simple content-related n-grams and POS tagging had been proven insufficient for fake news classification. However, they did not realise any empirical research results, which could confirm these statements experimentally in the last decade. Considering this contradiction, the main aim of the paper is to experimentally evaluate the potential of the common use of n-grams and POS tags for the correct classification of fake and true news. The dataset of published fake or real news about the current Covid-19 pandemic was pre-processed using morphological analysis. As a result, n-grams of POS tags were prepared and further analysed. Three techniques based on POS tags were proposed and applied to different groups of n-grams in the pre-processing phase of fake news detection. The n-gram size was examined as the first. Subsequently, the most suitable depth of the decision trees for sufficient generalization was scoped. Finally, the performance measures of models based on the proposed techniques were compared with the standardised reference TF-IDF technique. The performance measures of the model like accuracy, precision, recall and f1-score are considered, together with the 10-fold cross-validation technique. Simultaneously, the question, whether the TF-IDF technique can be improved using POS tags was researched in detail. The results showed that the newly proposed techniques are comparable with the traditional TF-IDF technique. At the same time, it can be stated that the morphological analysis can improve the baseline TF-IDF technique. As a result, the performance measures of the model, precision for fake news and recall for real news, were statistically significantly improved.

• Klíčová slova
• Fake news identification, Morphological analysis, Natural language processing, POS tagging, Text mining,
• Publikační typ
• zprávy MeSH

Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.

• Klíčová slova
• COVID-19, Domain-independent knowledge graph, Influential scholarly document prediction, Machine learning algorithms, Text mining, World health organization,
• MeSH
• algoritmy MeSH
• COVID-19 * MeSH
• jazyk (prostředek komunikace) MeSH
• lidé MeSH
• rozpoznávání automatizované * MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the current research collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins--all to all protein similarity. For the calculation of protein similarity, we use term frequency × inverse document frequency ( tf × idf ) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision. We have chosen the structural classification of proteins (SCOP) database for verification of the precision of our algorithm because it is maintained primarily by humans. The next success of this paper would be the ability to determine SCOP categories of proteins not included in the latest version of the SCOP database (v. 1.75) with nearly 100% precision.

• MeSH
• algoritmy * MeSH
• data mining metody   MeSH
• databáze proteinů MeSH
• lidé MeSH
• proteiny chemie   MeSH
• reprodukovatelnost výsledků MeSH
• strukturní homologie proteinů * MeSH
• terciární struktura proteinů * MeSH
• umělá inteligence MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• proteiny MeSH

In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classification.

• Klíčová slova
• COVID-19, Machine learning algorithms, Multi-class classification, Text mining,
• Publikační typ
• časopisecké články MeSH

Multiple studies have investigated bibliometric factors predictive of the citation count a research article will receive. In this article, we go beyond bibliometric data by using a range of machine learning techniques to find patterns predictive of citation count using both article content and available metadata. As the input collection, we use the CORD-19 corpus containing research articles-mostly from biology and medicine-applicable to the COVID-19 crisis. Our study employs a combination of state-of-the-art machine learning techniques for text understanding, including embeddings-based language model BERT, several systems for detection and semantic expansion of entities: ConceptNet, Pubtator and ScispaCy. To interpret the resulting models, we use several explanation algorithms: random forest feature importance, LIME, and Shapley values. We compare the performance and comprehensibility of models obtained by "black-box" machine learning algorithms (neural networks and random forests) with models built with rule learning (CORELS, CBA), which are intrinsically explainable. Multiple rules were discovered, which referred to biomedical entities of potential interest. Of the rules with the highest lift measure, several rules pointed to dipeptidyl peptidase4 (DPP4), a known MERS-CoV receptor and a critical determinant of camel to human transmission of the camel coronavirus (MERS-CoV). Some other interesting patterns related to the type of animal investigated were found. Articles referring to bats and camels tend to draw citations, while articles referring to most other animal species related to coronavirus are lowly cited. Bat coronavirus is the only other virus from a non-human species in the betaB clade along with the SARS-CoV and SARS-CoV-2 viruses. MERS-CoV is in a sister betaC clade, also close to human SARS coronaviruses. Thus both species linked to high citation counts harbor coronaviruses which are more phylogenetically similar to human SARS viruses. On the other hand, feline (FIPV, FCOV) and canine coronaviruses (CCOV) are in the alpha coronavirus clade and more distant from the betaB clade with human SARS viruses. Other results include detection of apparent citation bias favouring authors with western sounding names. Equal performance of TF-IDF weights and binary word incidence matrix was observed, with the latter resulting in better interpretability. The best predictive performance was obtained with a "black-box" method-neural network. The rule-based models led to most insights, especially when coupled with text representation using semantic entity detection methods. Follow-up work should focus on the analysis of citation patterns in the context of phylogenetic trees, as well on patterns referring to DPP4, which is currently considered as a SARS-Cov-2 therapeutic target.

• Klíčová slova
• Bibliometry, CORD-19: COVID-19 open research dataset, Citation prediction, Interpretability, Phylogenetic distance, SARS-CoV-2, Text analysis, Virus clades,
• Publikační typ
• časopisecké články MeSH