Formal Concept Analysis Applications in Bioinformatics

The bioinformatics discipline seeks to solve problems in biology with computational theories and methods. Formal concept analysis (FCA) is one such theoretical model, based on partial orders. FCA allows the user to examine the structural properties of data based on which subsets of the dataset depend on each other. This article surveys the current literature related to the use of FCA for bioinformatics. The survey begins with a discussion of FCA, its hierarchical advantages, several advanced models of FCA, and lattice management strategies. It then examines how FCA has been used in bioinformatics applications, followed by future prospects of FCA in those areas. The applications addressed include gene data analysis (with next-generation sequencing), biomarkers discovery, protein-protein interaction, disease analysis (including COVID-19, cancer, and others), drug design and development, healthcare informatics, biomedical ontologies, and phylogeny. Some of the most promising prospects of FCA are identifying influential nodes in a network representing protein-protein interactions, determining critical concepts to discover biomarkers, integrating machine learning and deep learning for cancer classification, and pattern matching for next-generation sequencing. [ FROM AUTHOR]

Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model.

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease.

How to provide light to COVID data by means of FCA

COVID data are usually presented in a non-structured format and mainly focused on healthy issues (incidence, mortality, etc). At the same time, Governments have designed a set of measures to deal with the Pandemic. In addition, several institutions have studied the economical effects of the situation in each country. In this work, we combine these three data sources and illustrate how Formal Concept Analysis can become a useful tool to discover relationships among these three views of the situation: health, politics and economy. Our aim is to provide an implication-driven approach to discover knowledge behind the data. © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic.

The spread of Covid-19 profoundly changed citizens' daily lives due to the introduction of new modes of work and access to services based on smart technologies. Although the relevance of new technologies as strategic levers for crisis resolution has been widely debated before the pandemic, especially in the smart cities' context, how individuals have agreed to include the technological changes dictated by the pandemic in their daily interactions remains an open question. This paper aims at detecting citizens' sentiment toward technology before and after the emergence of the Covid-19 pandemic using Fuzzy Formal Concept Analysis (FFCA) to analyze a large corpus of tweets. Specifically, citizens' attitudes in five cities (Berlin, Dublin, London, Milan, and Madrid) were explored to extract and classify the key topics related to the degree of confidence, familiarity and approval of new technologies. The results shed light on the complex technology acceptance process and help managers identify the potential negative effects of smart technologies. In this way, the study enhances scholars' and practitioners' understanding of the strategies for enabling the use of technology within smart cities to manage the transformations introduced by the health emergency and guide citizens' behaviour.

A rational strategy to support approved COVID-19 vaccines prioritization.

The World Health Organization (WHO) proposed a set of criteria to be considered for the prioritization of COVID-19 candidate vaccines for further development of phase II/III clinical trials, thinking in a target audience that includes vaccine scientists, product developers, manufacturers, regulators, and funding agencies. In this paper, a knowledge-based or rational strategy is employed to perform a prioritization matrix of approved COVID-19 vaccines: BBIBP-CorV, JANSSEN, CORONAVAC, SPUTNIK V, MODERNA, PFIZER, and VAXZEVRIA, based on those proposed criteria by WHO, related to safety, efficacy, stability, implementation, and availability. We found that JANSSEN vaccine is the one with the highest score in the present study, but our analysis suggests that the WHO criteria could be more useful if they are considered separately, taking into account the social, demographic and economic characteristics of each country.